Zheng, Y., Koh, H. Y., Ju, J., Nguyen, A. T. N., May, L. T., Webb, G. I., & Pan, S. (in press). Large language models for scientific discovery in molecular property prediction. Nature Machine Intelligence.
Pan, T., Bi, Y., Wang, X., Zhang, Y., Webb, G. I., Gasser, R. B., Kurgan, L., & Song, J. (in press). SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations. Genomics, Proteomics & Bioinformatics.
Darban, Z. Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2025). CARLA: Self-supervised contrastive representation learning for time series anomaly detection. Pattern Recognition, 157, Art. no. 110874.
Ramakrishnaiah, Y., Macesic, N., Webb, G. I., Peleg, A. Y., & Tyagi, S. (2025). EHR-ML: A Data-Driven Framework for Designing Machine Learning Applications with Electronic Health Records. International Journal of Medical Informatics, 105816.
Tan, C. W., Herrmann, M., Salehi, M., & Webb, G. I. (2025). Proximity forest 2.0: a new effective and scalable similarity-based classifier for time series. Data Mining and Knowledge Discovery, 39(2), Art. no. 14.
Yao, S., Huang, Y., Wang, X., Zhang, Y., Paixao, I. C., Wang, Z., Chai, C. L., Wang, H., Lu, D., Webb, G. I., Li, S., Guo, Y., Chen, Q., & Song, J. (2025). A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors. Scientific Data, 12, Art. no. 88.
Zhang, H., Jiang, L., Zhang, W., & Webb, G. I. (2025). Dual-View Learning from Crowds. ACM Transactions on Knowledge Discovery from Data, 19(3), 1–21.
Zamanzadeh Darban, Z., Webb, G. I., Pan, S., Aggarwal, C., & Salehi, M. (2024). Deep Learning for Time Series Anomaly Detection: A Survey. ACM Computing Surveys, 57(1), 1–42.
Foumani, N. M., Miller, L., Tan, C. W., Webb, G. I., Forestier, G., & Salehi, M. (2024). Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey. ACM Computing Surveys.
Foumani, N. M., Tan, C. W., Webb, G. I., Rezatofighi, H., & Salehi, M. (2024). Series2vec: similarity-based self-supervised representation learning for time series classification. Data Mining and Knowledge Discovery.
Koh, H. Y., Nguyen, A. T. N., Pan, S., May, L. T., & Webb, G. I. (2024). Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data. Nature Machine Intelligence, 6, 673–687.
Jin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S. (2024). A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-20.
Foumani, N. M., Tan, C. W., Webb, G. I., & Salehi, M. (2024). Improving position encoding of transformers for multivariate time series classification. Data Mining and Knowledge Discovery, 38, 22-48.
Nguyen, A. T. N., Nguyen, D. T. N., Koh, H. Y., Toskov, J., MacLean, W., Xu, A., Zhang, D., Webb, G. I., May, L. T., & Halls, M. L. (2024). The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery. British Journal of Pharmacology, 181(14), 2371-2384.
Nguyen, H., Peleg, A. Y., Song, J., Antony, B., Webb, G. I., Wisniewski, J. A., Blakeway, L. V., Badoordeen, G. Z., Theegala, R., Zisis, H., Dowe, D. L., & Macesic, N. (2024). Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra. mSystems.
Lee, L. K., Webb, G. I., Schmidt, D. F., & Piatkowski, N. (2024). Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation. Knowledge and Information Systems, 66, 7527-7556.
Bi, Y., Li, F., Wang, C., Pan, T., Davidovich, C., Webb, G., & Song, J. (2024). Advancing microRNA target site prediction with transformer and base-pairing patterns. Nucleic Acids Research, 52(19), 11455–11465.
Li, S., Liu, Y., Chen, Q., Webb, G. I., & Pan, S. (2024). Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 1255-1265.
Gao, M., Zhang, D., Chen, Y., Zhang, Y., Wang, Z., Wang, X., Li, S., Guo, Y., Webb, G. I., Nguyen, A. T. N., May, L., & Song, J. (2024). GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction. Computers in Biology and Medicine, 108339.
Dempster, A., Schmidt, D. F., & Webb, G. I. (2024). Quant: a minimalist interval method for time series classification. Data Mining and Knowledge Discovery, 38, 2377-2402.
Liu, T., Webb, G., Yue, L., & Wang, D. (Ed). (2024). AI 2023: Advances in Artificial Intelligence: 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28 December 1, 2023, Proceedings, Part I. Springer Nature Singapore.
Miller, L., Pelletier, C., & Webb, G. I. (2024). Deep Learning for Satellite Image Time-Series Analysis: A review. IEEE Geoscience and Remote Sensing Magazine, 12(3), 81-124.
Lee, L. K., Piatkowski, N., Petitjean, F., & Webb, G. I. (2023). Computing Divergences between Discrete Decomposable Models. Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 12243-12251.
Jung, M., Lukose, D., Nielsen, S., Bell, S. J., Webb, G. I., & Ilomaki, J. (2023). COVID-19 restrictions and the incidence and prevalence of prescription opioid use in Australia - a nation-wide study. British Journal of Clinical Pharmacology, 89(2), 914-920.
Herrmann, M., & Webb, G. I. (2023). Amercing: An Intuitive and Effective Constraint for Dynamic Time Warping. Pattern Recognition, 137, Art. no. 109333.
Godahewa, R., Bergmeir, C., Webb, G. I., & Montero-Manso, P. (2023). An accurate and fully-automated ensemble model for weekly time series forecasting. International Journal of Forecasting, 39, 641-658.
Tan, C. W., Herrmann, M., & Webb, G. I. (2023). Ultra-fast meta-parameter optimization for time series similarity measures with application to nearest neighbour classification. Knowledge and Information Systems.
Shifaz, A., Pelletier, C., Petitjean, F., & Webb, G. I. (2023). Elastic similarity and distance measures for multivariate time series. Knowledge and Information Systems, 65, 2665-2698.
Lucas, B., Pelletier, C., Schmidt, D., Webb, G. I., & Petitjean, F. (2023). A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping. Machine Learning, 112, 1941-1973.
Pan, T., Li, C., Bi, Y., Wang, Z., Gasser, R. B., Purcell, A. W., Akutsu, T., Webb, G. I., Imoto, S., & Song, J. (2023). PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships. Bioinformatics, Art. no. btad094.
Godahewa, R., Webb, G. I., Schmidt, D., & Bergmeir, C. (2023). SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting. Machine Learning, 112, 2555-2591.
Zhang, H., Jiang, L., & Webb, G. I. (2023). Rigorous non-disjoint discretization for naive Bayes. Pattern Recognition, 140, Art. no. 109554.
Miller, L., Zhu, L., Yebra, M., Rudiger, C., & Webb, G. I. (2023). Projecting live fuel moisture content via deep learning. International Journal of Wildland Fire.
Herrmann, M., Tan, C. W., & Webb, G. I. (2023). Parameterizing the cost function of dynamic time warping with application to time series classification. Data Mining and Knowledge Discovery, 37, 2024-2045.
Li, F., Wang, C., Guo, X., Akutsu, T., Webb, G. I., Coin, L. J. M., Kurgan, L., & Song, J. (2023). ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction. Briefings in Bioinformatics, 24(6), Art. no. bbad372.
Huynh, V., Say, B., Vogel, P., Cao, L., Webb, G. I., & Aleti, A. (2023). Rapid Identification of Protein Formulations with Bayesian Optimisation. 2023 International Conference on Machine Learning and Applications (ICMLA), pp. 776-781.
Zhu, Y., Li, F., Guo, X., Wang, X., Coin, L. J. M., Webb, G. I., Song, J., & Jia, C. (2023). TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters. Briefings in Bioinformatics, 24(4), Art. no. bbad209.
Ismail-Fawaz, A., Ismail Fawaz, H., Petitjean, F., Devanne, M., Weber, J., Berretti, S., Webb, G. I., & Forestier, G. (2023). ShapeDBA: Generating Effective Time Series Prototypes Using ShapeDTW Barycenter Averaging. Advanced Analytics and Learning on Temporal Data, Cham, pp. 127–142.
Lee, L. K., Webb, G. I., Schmidt, D. F., & Piatkowski, N. (2023). Computing Marginal and Conditional Divergences between Decomposable Models with Applications. Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 239-248.
Pialla, G., Ismail Fawaz, H., Devanne, M., Weber, J., Idoumghar, L., Muller, P., Bergmeir, C., Schmidt, D. F., Webb, G. I., & Forestier, G. (2023). Time series adversarial attacks: an investigation of smooth perturbations and defense approaches. International Journal of Data Science and Analytics.
Ramakrishnaiah, Y., Macesic, N., Webb, G., Peleg, A. Y., & Tyagi, S. (2023). EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes. Journal of Biomedical Informatics, 104509.
Dempster, A., Schmidt, D. F., & Webb, G. I. (2023). HYDRA: competing convolutional kernels for fast and accurate time series classification. Data Mining and Knowledge Discovery, 37(5), 1779-1805.
Bi, Y., Li, F., Guo, X., Wang, Z., Pan, T., Guo, Y., Webb, G. I., Yao, J., Jia, C., & Song, J. (2022). Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations. Briefings in Bioinformatics, 23(6), Art. no. bbac467.
Wang, Y., Hu, C., Kwok, T., Bain, C. A., Xue, X., Gasser, R. B., Webb, G. I., Boussioutas, A., Shen, X., Daly, R. J., & Song, J. (2022). DEMoS: A Deep Learning-based Ensemble Approach for Predicting the Molecular Subtypes of Gastric Adenocarcinomas from Histopathological Images. Bioinformatics, 38(17), 4206-4213.
Livori, A. C., Lukose, D., Bell, S. J., Webb, G. I., & Ilomaki, J. (2022). Did Australia's COVID-19 restrictions impact statin incidence, prevalence or adherence?. Current Problems in Cardiology, 101576.
Jung, M., Lukose, D., Nielsen, S., Bell, S. J., Webb, G. I., & Ilomaki, J. (2022). Incidence and prevalence of prescription opioid use during Australian COVID-19 restrictions. Pharmacoepidemiology and Drug Safety, 31(S2 ABSTRACTS of ICPE 2022, the 38th International Conference on Pharmacoepidemiology and Therapeutic Risk Management), 3-628.
Manapragada, C., Salehi, M., & Webb, G. I. (2022). Extremely Fast Hoeffding Adaptive Tree. Proceedings of the 2022 IEEE International Conference on Data Mining (ICDM), pp. 319-328.
ICDM-2022 Best Paper Runner-up Award
Iqbal, S., Ge, F., Li, F., Akutsu, T., Zheng, Y., Gasser, R. B., Yu, D., Webb, G. I., & Song, J. (2022). PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations. Journal of Chemical Information and Modeling, 62(17), 4270-4282.
Miller, L., Zhu, L., Yebra, M., Rudiger, C., & Webb, G. I. (2022). Multi-modal temporal CNNs for live fuel moisture content estimation. Environmental Modelling & Software, 105467.
Chen, Z., Liu, X., Li, F., Li, C., Marquez-Lago, T., Leier, A., Webb, G. I., Xu, D., Akutsu, T., & Song, J. (2022). Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL. In KC, D. B. (Ed.), In Computational Methods for Predicting Post-Translational Modification Sites (, pp. 205-219). New York, NY: Springer US.
Wang, X., Li, F., Xu, J., Rong, J., Webb, G. I., Ge, Z., Li, J., & Song, J. (2022). ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning. Briefings in Bioinformatics, 23(2), Art. no. bbac031.
Tan, C. W., Dempster, A., Bergmeir, C., & Webb, G. I. (2022). MultiRocket: multiple pooling operators and transformations for fast and effective time series classification. Data Mining and Knowledge Discovery, 36, 1623-1646.
Wang, Y., Wang, Y. G., Hu, C., Li, M., Fan, Y., Otter, N., Sam, I., Gou, H., Hu, Y., Kwok, T., Zalcberg, J., Boussioutas, A., Daly, R. J., Montfar, G., Li, P., Xu, D., Webb, G. I., & Song, J. (2022). Cell graph neural networks enable the precise prediction of patient survival in gastric cancer. npj Precision Oncology, 6(1), Art. no. 45.
Pialla, G., Ismail Fawaz, H., Devanne, M., Weber, J., Idoumghar, L., Muller, P., Bergmeir, C., Schmidt, D., Webb, G. I., & Forestier, G. (2022). Smooth Perturbations for Time Series Adversarial Attacks. Proceedings of the 2022 Pacific-Asia Conference on Knowledge Discovery and Data Mining, Cham, pp. 485-496.
Zhang, M., Jia, C., Li, F., Li, C., Zhu, Y., Akutsu, T., Webb, G. I., Zou, Q., Coin, L. J. M., & Song, J. (2022). Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction. Briefings in Bioinformatics, 23, Art. no. bbab551.
Manapragada, C., Gomes, H. M., Salehi, M., Bifet, A., & Webb, G. I. (2022). An eager splitting strategy for online decision trees in ensembles. Data Mining and Knowledge Discovery, 36, 566-619.
Li, F., Dong, S., Leier, A., Han, M., Guo, X., Xu, J., Wang, X., Pan, S., Jia, C., Zhang, Y., Webb, G. I., Coin, L. J. M., Li, C., & Song, J. (2022). Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Briefings in Bioinformatics, 23(1), Art. no. bbab461.
Wang, Y., Yang, L., Webb, G. I., Ge, Z., & Song, J. (2021). OCTID: a one-class learning-based Python package for tumor image detection. Bioinformatics, 37(21), 3986-3988.
Tan, C. W., Bergmeir, C., Petitjean, F., & Webb, G. I. (2021). Time series extrinsic regression. Data Mining and Knowledge Discovery, 35(3), 1032-1060.
Li, M., Wang, Y., Li, F., Zhao, Y., Liu, M., Zhang, S., Bin, Y., Smith, A. I., Webb, G., Li, J., Song, J., & Xia, J. (2021). A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction. IEEE/ACM Trans Comput Biol Bioinform, 18, 1801-1810.
Wang, Y., Li, F., Bharathwaj, M., Rosas, N. C., Leier, A., Akutsu, T., Webb, G. I., Marquez-Lago, T. T., Li, J., Lithgow, T., & Song, J. (2021). DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases. Briefings in Bioinformatics, 22(4), Art. no. bbaa301.
Krempl, G., Hofer, V., Webb, G., & Hullermeier, E. (2021). Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372) Schloss Dagstuhl - Leibniz-Zentrum fur Informatik.
Chen, Z., Zhao, P., Li, C., Li, F., Xiang, D., Chen, Y., Akutsu, T., Daly, R. J., Webb, G. I., Zhao, Q., Kurgan, L., & Song, J. (2021). iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization. Nucleic Acids Research, 49(10), Art. no. e60.
Clarivate Web of Science Highly Cited Paper 2022 - 2024
Boley, M., Teshuva, S., Bodic, P. L., & Webb, G. I. (2021). Better Short than Greedy: Interpretable Models through Optimal Rule Boosting. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp. 351-359.
Wang, Y., Coudray, N., Zhao, Y., Li, F., Hu, C., Zhang, Y., Imoto, S., Tsirigos, A., Webb, G. I., Daly, R. J., & Song, J. (2021). HEAL: an automated deep learning framework for cancer histopathology image analysis. Bioinformatics, 37(22), 4291-4295.
Mei, S., Li, F., Xiang, D., Ayala, R., Faridi, P., Webb, G. I., Illing, P. T., Rossjohn, J., Akutsu, T., Croft, N. P., Purcell, A. W., & Song, J. (2021). Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules. Briefings in Bioinformatics, 22.
Iqbal, S., Li, F., Akutsu, T., Ascher, D. B., Webb, G. I., & Song, J. (2021). Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations. Briefings in Bioinformatics, 22(6), Art. no. bbab184.
Dempster, A., Schmidt, D. F., & Webb, G. I. (2021). MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification. Proceedings of the 27thACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 248-257.
Zhu, L., Webb, G. I., Yebra, M., Scortechini, G., Miller, L., & Petitjean, F. (2021). Live fuel moisture content estimation from MODIS: A deep learning approach. ISPRS Journal of Photogrammetry and Remote Sensing, 179, 81-91.
Godahewa, R., Bandara, K., Webb, G. I., Smyl, S., & Bergmeir, C. (2021). Ensembles of localised models for time series forecasting. Knowledge-Based Systems, 233, Art. no. 107518.
Tan, C. W., Herrmann, M., & Webb, G. I. (2021). Ultra fast warping window optimization for Dynamic Time Warping. IEEE International Conference on Data Mining (ICDM-21), pp. 589-598.
Webb, G. I., & Petitjean, F. (2021). Tight lower bounds for Dynamic Time Warping. Pattern Recognition, 115, Art. no. 107895.
Herrmann, M., & Webb, G. I. (2021). Early abandoning and pruning for elastic distances including dynamic time warping. Data Mining and Knowledge Discovery, 35(6), 2577-2601.
Chen, Z., Zhao, P., Li, F., Leier, A., Marquez-Lago, T. T., Webb, G. I., Baggag, A., Bensmail, H., & Song, J. (2020). PROSPECT: A web server for predicting protein histidine phosphorylation sites. Journal of Bioinformatics and Computational Biology, 18(4), Art. no. 2050018.
Webb, G. I., Zhang, Z., Tseng, V. S., Williams, G., Vlachos, M., & Cao, L. (Ed). (2020). IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE Computer Society.
Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P., & Petitjean, F. (2020). InceptionTime: Finding AlexNet for Time Series Classification. Data Mining and Knowledge Discovery, 34, 1936-1962.
Clarivate Web of Science Highly Cited Paper 2022 - 2024
Dempster, A., Petitjean, F., & Webb, G. I. (2020). ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery, 34, 1454-1495.
Second Most Highly Cited Paper Published in Data Mining and Knowledge Discovery in 2020; Clarivate Web of Science Highly Cited Paper 2024
Li, F., Leier, A., Liu, Q., Wang, Y., Xiang, D., Akutsu, T., Webb, G. I., Smith, I. A., Marquez-Lago, T., Li, J., & Song, J. (2020). Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information. Genomics, Proteomics & Bioinformatics, 18(1), 52-64.
Miller, L., Bolton, M., Boulton, J., Mintrom, M., Nicholson, A., Rüdiger, C., Skinner, R., Raven, R., & Webb, G. I. (2020). AI for monitoring the Sustainable Development Goals and supporting and promoting action and policy development. IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G), pp. 180-185.
Shifaz, A., Pelletier, C., Petitjean, F., & Webb, G. I. (2020). TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification. Data Mining and Knowledge Discovery, 34(3), 742-775.
Third Most Highly Cited Paper Published in Data Mining and Knowledge Discovery in 2020
Zaidi, N. A., Du, Y., & Webb, G. I. (2020). On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers. IEEE Access, 8, 198856-198871.
Tan, C. W., Petitjean, F., & Webb, G. I. (2020). FastEE: Fast Ensembles of Elastic Distances for time series classification. Data Mining and Knowledge Discovery, 34(1), 231-272.
Nguyen, K., Le, T., Nguyen, T., Webb, G., & Phung, D. (2020). Robust Variational Learning for Multiclass Kernel Models with Stein Refinement. IEEE Transactions on Knowledge & Data Engineering, 34(9), 4425-4438.
Lucas, B., Pelletier, C., Schmidt, D., Webb, G. I., & Petitjean, F. (2020). Unsupervised Domain Adaptation Techniques for Classification of Satellite Image Time Series. IEEE International Geoscience and Remote Sensing Symposium, pp. 1074–1077.
Pratama, M., Pedrycz, W., & Webb, G. I. (2020). An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams. IEEE Transactions on Fuzzy Systems, 28(7), 1315-1328.
Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naive Bayes algorithm. Knowledge-Based Systems, 192, Art. no. 105361.
Li, F., Fan, C., Marquez-Lago, T. T., Leier, A., Revote, J., Jia, C., Zhu, Y., Smith, I. A., Webb, G. I., Liu, Q., Wei, L., Li, J., & Song, J. (2020). PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. Briefings in Bioinformatics, 21(3), 1069-1079.
Fischer, R., Piatkowski, N., Pelletier, C., Webb, G. I., Petitjean, F., & Morik, K. (2020). No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image Series. IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 546-555.
Goldenberg, I., & Webb, G. I. (2020). PCA-based drift and shift quantification framework for multidimensional data. Knowledge and Information Systems, 62, 2835-2854.
Li, F., Chen, J., Leier, A., Marquez-Lago, T., Liu, Q., Wang, Y., Revote, J., Smith, I. A., Akutsu, T., Webb, G. I., Kurgan, L., & Song, J. (2020). DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites. Bioinformatics, 36(4), 1057-1065.
Clarivate Web of Science Highly Cited Paper 2020
Phung, D., Webb, G. I., & Sammut, C. (Ed). (2020). Encyclopedia of Machine Learning and Data Science. Springer US.
Chen, Z., Zhao, P., Li, F., Marquez-Lago, T. T., Leier, A., Revote, J., Zhu, Y., Powell, D. R., Akutsu, T., Webb, G. I., Chou, K., Smith, I. A., Daly, R. J., Li, J., & Song, J. (2020). iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Briefings in Bioinformatics, 21(3), 1047-1057.
Clarivate Web of Science Highly Cited Paper 2020 - 2024
Chen, Z., Zhao, P., Li, F., Wang, Y., Smith, I. A., Webb, G. I., Akutsu, T., Baggag, A., Bensmail, H., & Song, J. (2020). Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences. Briefings in Bioinformatics, 21(5), 1676-1696.
Wang, X., Li, C., Li, F., Sharma, V. S., Song, J., & Webb, G. I. (2019). SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models. BMC Bioinformatics, 20(1), Art. no. 602.
Pelletier, C., Webb, G. I., & Petitjean, F. (2019). Deep Learning for the Classification of Sentinel-2 Image Series. IEEE International Geoscience And Remote Sensing Symposium.
Li, F., Zhang, Y., Purcell, A. W., Webb, G. I., Chou, K., Lithgow, T., Li, C., & Song, J. (2019). Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinformatics, 20(1), 112.
Hamalainen, W., & Webb, G. I. (2019). A tutorial on statistically sound pattern discovery. Data Mining and Knowledge Discovery, 33(2), 325-377.
Wang, J., Yang, B., An, Y., Marquez-Lago, T., Leier, A., Wilksch, J., Hong, Q., Zhang, Y., Hayashida, M., Akutsu, T., Webb, G. I., Strugnell, R. A., Song, J., & Lithgow, T. (2019). Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches. Briefings in Bioinformatics, 20(3), 931-951.
Tan, C. W., Petitjean, F., & Webb, G. I. (2019). Elastic bands across the path: A new framework and methods to lower bound DTW. Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 522-530.
Chen, Z., Li, L., Xu, D., Chou, K., Liu, X., Smith, A. I., Li, F., Song, J., Li, C., Leier, A., Marquez-Lago, T., Akutsu, T., & Webb, G. I. (2019). Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Briefings in Bioinformatics, 20(6), 2267-2290.
Song, J., Wang, Y., Li, F., Akutsu, T., Rawlings, N. D., Webb, G. I., & Chou, K. (2019). iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Briefings in Bioinformatics, 20(2), 638-658.
Clarivate Web of Science Hot Paper 2019
Yu, H., & Webb, G. I. (2019). Adaptive Online Extreme Learning Machine by Regulating Forgetting Factor by Concept Drift Map. Neurocomputing, 343, 141-153.
Goldenberg, I., & Webb, G. I. (2019). Survey of distance measures for quantifying concept drift and shift in numeric data. Knowledge and Information Systems, 60(2), 591-615.
Li, F., Wang, Y., Li, C., Marquez-Lago, T. T., Leier, A., Rawlings, N. D., Haffari, G., Revote, J., Akutsu, T., Chou, K., Purcell, A. W., Pike, R. N., Webb, G. I., Smith, I. A., Lithgow, T., Daly, R. J., Whisstock, J. C., & Song, J. (2019). Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods. Briefings in Bioinformatics, 20(6), 2150-2166.
Lucas, B., Shifaz, A., Pelletier, C., O'Neill, L., Zaidi, N., Goethals, B., Petitjean, F., & Webb, G. I. (2019). Proximity Forest: an effective and scalable distance-based classifier for time series. Data Mining and Knowledge Discovery, 33, 607-635.
Lucas, B., Pelletier, C., Inglada, J., Schmidt, D., Webb, G. I., & Petitjean, F. (2019). Exploring Data Quantity Requirements for Domain Adaptation in the Classification of Satellite Image Time Series. Proceedings 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2019.
Zhang, Y., Xie, R., Wang, J., Leier, A., Marquez-Lago, T. T., Akutsu, T., Webb, G. I., Chou, K., & Song, J. (2019). Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Briefings in Bioinformatics, 20(6), 2185-2199.
Pelletier, C., Ji, Z., Hagolle, O., Morse-McNabb, E., Sheffield, K., Webb, G. I., & Petitjean, F. (2019). Using Sentinel-2 Image Time Series to map the State of Victoria, Australia. Proceedings 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2019.
Pelletier, C., Webb, G. I., & Petitjean, F. (2019). Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sensing, 11(5), Art. no. 523.
Clarivate Web of Science Highly Cited Paper 2021 - 2024
Wang, H., Feng, L., Webb, G. I., Kurgan, L., Song, J., & Lin, D. (2018). Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity. Briefings in Bioinformatics, 19(5), 838-852.
Shi, W., Zhang, A., & Webb, G. I. (2018). Mining significant crisp-fuzzy spatial association rules. International Journal of Geographical Information Science, 32(6), 1247-1270.
An, Y., Wang, J., Li, C., Leier, A., Marquez-Lago, T., Wilksch, J., Zhang, Y., Webb, G. I., Song, J., & Lithgow, T. (2018). Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI. Briefings in Bioinformatics, 19(1), 148-161.
Manapragada, C., Webb, G. I., & Salehi, M. (2018). Extremely Fast Decision Tree. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 1953–1962.
Chen, Z., Zhao, P., Li, F., Leier, A., Marquez-Lago, T. T., Wang, Y., Webb, G. I., Smith, I. A., Daly, R. J., Chou, K., & Song, J. (2018). iFeature: A python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics, 2499-2502.
Clarivate Web of Science Highly Cited Paper 2019 - 2024
Nguyen, K., Le, T., Nguyen, T. D., Phung, D., & Webb, G. I. (2018). Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 2003–2011.
Webb, G. I., Lee, L. K., Goethals, B., & Petitjean, F. (2018). Analyzing concept drift and shift from sample data. Data Mining and Knowledge Discovery, 32(5), 1179-1199.
Phung, D., Tseng, V. S., Webb, G. I., Ho, B., Ganji, M., & Rashidi, L. (Ed). (2018). Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings (Vol. 10939). Springer.
Li, C., Clark, L. V. T., Zhang, R., Porebski, B. T., McCoey, J. M., Borg, N. A., Webb, G. I., Kass, I., Buckle, M., Song, J., Woolfson, A., & Buckle, A. M. (2018). Structural Capacitance in Protein Evolution and Human Diseases. Journal of Molecular Biology, 430(18), 3200-3217.
Petitjean, F., Buntine, W., Webb, G. I., & Zaidi, N. (2018). Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes. Machine Learning, 107(8-10), 1303-1331.
Zaidi, N. A., Webb, G. I., Petitjean, F., & Forestier, G. (2018). On the Inter-Relationships among Drift Rate, Forgetting Rate, Bias/Variance Profile and Error. arxiv, 1801.09354.
Tan, C. W., Webb, G. I., Petitjean, F., & Reichl, P. (2018). Tamping Effectiveness Prediction Using Supervised Machine Learning Techniques. Proceedings of the First International Conference on Rail Transportation (ICRT-17).
Song, J., Li, F., Takemoto, K., Haffari, G., Akutsu, T., Chou, K. C., & Webb, G. I. (2018). PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. Journal of Theoretical Biology, 443, 125-137.
Clarivate Web of Science Hot Paper
Zaidi, N. A., Petitjean, F., & Webb, G. I. (2018). Efficient and Effective Accelerated Hierarchical Higher-Order Logistic Regression for Large Data Quantities. Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 459-467.
Tan, C. W., Herrmann, M., Forestier, G., Webb, G. I., & Petitjean, F. (2018). Efficient search of the best warping window for Dynamic Time Warping. Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 459-467.
Best Research Paper Award
Zaidi, N. A., & Webb, G. I. (2017). A Fast Trust-Region Newton Method for Softmax Logistic Regression. Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 705-713.
Forestier, G., Petitjean, F., Dau, H. A., Webb, G. I., & Keogh, E. (2017). Generating synthetic time series to augment sparse datasets. IEEE International Conference on Data Mining (ICDM-17), pp. 865-870.
Song, J., Li, C., Zheng, C., Revote, J., Zhang, Z., & Webb, G. I. (2017). MetalExplorer, a Bioinformatics Tool for the Improved Prediction of Eight Types of Metal-binding Sites Using a Random Forest Algorithm with Two-step Feature Selection. Current Bioinformatics, 12(6), 480-489.
Song, J., Li, F., Leier, A., Marquez-Lago, T. T., Akutsu, T., Haffari, G., Chou, K., Webb, G. I., & Pike, R. N. (2017). PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy. Bioinformatics, 34(4), 684-687.
Clarivate Web of Science Highly Cited Paper 2019, 2020, 2021
Chen, S., Martinez, A., Webb, G., & Wang, L. (2017). Sample-based Attribute Selective AnDE for Large Data. IEEE Transactions on Knowledge and Data Engineering, 29(1), 172-185.
Hamalainen, W., & Webb, G. I. (2017). Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining. Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 309-317.
An, Y., Wang, J., Li, C., Revote, J., Zhang, Y., Naderer, T., Hayashida, M., Akutsu, T., Webb, G. I., Lithgow, T., & Song, J. (2017). SecretEPDB: a comprehensive web-based resource for secreted effector proteins of the bacterial types III, IV and VI secretion systems. Scientific Reports, 7, Art. no. 41031.
Fernando, T. L., & Webb, G. I. (2017). SimUSF: an efficient and effective similarity measure that is invariant to violations of the interval scale assumption. Data Mining and Knowledge Discovery, 31(1), 264-286.
Song, J., Wang, H., Wang, J., Leier, A., Marquez-Lago, T., Yang, B., Zhang, Z., Akutsu, T., Webb, G. I., & Daly, R. J. (2017). PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection. Scientific Reports, 7(1), Art. no. 6862.
Ananda-Rajah, M. R., Bergmeir, C., Petitjean, F., Slavin, M. A., Thursky, K. A., & Webb, G. I. (2017). Toward Electronic Surveillance of Invasive Mold Diseases in Hematology-Oncology Patients: An Expert System Combining Natural Language Processing of Chest Computed Tomography Reports, Microbiology, and Antifungal Drug Data. JCO Clinical Cancer Informatics(1), 1-10.
Wang, Y., Song, J., Marquez-Lago, T. T., Leier, A., Li, C., Lithgow, T., Webb, G. I., & Shen, H. (2017). Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites. Scientific Reports, 7, Art. no. 5755.
Bergmeir, C., Bilgrami, I., Bain, C., Webb, G. I., Orosz, J., & Pilcher, D. (2017). Designing a more efficient, effective and safe Medical Emergency Team (MET) service using data analysis. PLoS ONE, 12(12), Art. no. e0188688.
Wang, J., Yang, B., Revote, J., Leier, A., Marquez-Lago, T. T., Webb, G. I., Song, J., Chou, K., & Lithgow, T. (2017). POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics, 33(17), 2756-2758.
Tan, C. W., Webb, G. I., & Petitjean, F. (2017). Indexing and classifying gigabytes of time series under time warping. Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 282-290.
Sammut, C., & Webb, G. I. (Ed). (2017). Encyclopedia of Machine Learning and Data Mining. Berlin: Springer.
Chen, S., Martinez, A. M., Webb, G. I., & Wang, L. (2017). Selective AnDE for large data learning: a low-bias memory constrained approach. Knowledge and Information Systems, 50(2), 475-503.
Zaidi, N., Webb, G. I., Carman, M., Petitjean, F., Buntine, W., Hynes, H., & De Sterck, H. (2017). Efficient Parameter Learning of Bayesian Network Classifiers. Machine Learning, 106(9-10), 1289-1329.
Petitjean, F., Li, T., Tatti, N., & Webb, G. I. (2016). Skopus: Mining top-k sequential patterns under leverage. Data Mining and Knowledge Discovery, 30(5), 1086-1111.
Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., & Petitjean, F. (2016). Characterizing Concept Drift. Data Mining and Knowledge Discovery, 30(4), 964-994.
Zhang, A., Shi, W., & Webb, G. I. (2016). Mining significant association rules from uncertain data. Data Mining and Knowledge Discovery, 30(4), 928-963.
Martinez, A. M., Webb, G. I., Chen, S., & Zaidi, N. A. (2016). Scalable Learning of Bayesian Network Classifiers. Journal of Machine Learning Research, 17(44), 1-35.
Petitjean, F., Forestier, G., Webb, G. I., Nicholson, A. E., Chen, Y., & Keogh, E. (2016). Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm. Knowledge and Information Systems, 47(1), 1-26.
Webb, G. I., & Petitjean, F. (2016). A multiple test correction for streams and cascades of statistical hypothesis tests. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-16, pp. 1255-1264.
Top reviewer score (4.75/5.0), shortlisted for best paper award and invited to ACM TKDE journal KDD-16 special issue
Petitjean, F., & Webb, G. I. (2016). Scalable Learning of Graphical Models. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-16, pp. 2131-2132.
Zaidi, N. A., Webb, G. I., Carman, M. J., Petitjean, F., & Cerquides, J. (2016). ALRn: Accelerated higher-order logistic regression. Machine Learning, 104(2), 151-194.
Li, F., Li, C., Revote, J., Zhang, Y., Webb, G. I., Li, J., Song, J., & Lithgow, T. (2016). GlycoMinestruct: a new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features. Scientific Reports, 6, Art. no. 34595.
Zaidi, N. A., Petitjean, F., & Webb, G. I. (2016). Preconditioning an Artificial Neural Network Using Naive Bayes. Proceedings of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, pp. 341-353.
Chang, C. C. H., Li, C., Webb, G. I., Tey, B., & Song, J. (2016). Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli. Scientific Reports, 6, Art. no. 21844.
Porebski, B. T., Keleher, S., Hollins, J. J., Nickson, A. A., Marijanovic, E. M., Borg, N. A., Costa, M. G. S., Pearce, M. A., Dai, W., Zhu, L., Irving, J. A., Hoke, D. E., Kass, I., Whisstock, J. C., Bottomley, S. P., Webb, G. I., McGowan, S., & Buckle, A. M. (2016). Smoothing a rugged protein folding landscape by sequence-based redesign. Scientific Reports, 6, Art. no. 33958.
Wang, H., Feng, L., Zhang, Z., Webb, G. I., Lin, D., & Song, J. (2016). Crysalis: an integrated server for computational analysis and design of protein crystallization. Scientific Reports, 6, Art. no. 21383.
Li, F., Li, C., Wang, M., Webb, G. I., Zhang, Y., Whisstock, J. C., & Song, J. (2015). GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome. Bioinformatics, 31(9), 1411-1419.
Cao, L., Zhang, C., Joachims, T., Webb, G. I., Margineantu, D. D., & Williams, G. (Ed). (2015). Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM.
Porebski, B. T., Nickson, A. A., Hoke, D. E., Hunter, M. R., Zhu, L., McGowan, S., Webb, G. I., & Buckle, A. M. (2015). Structural and dynamic properties that govern the stability of an engineered fibronectin type III domain. Protein Engineering, Design and Selection, 28(3), 67-78.
Petitjean, F., & Webb, G. I. (2015). Scaling log-linear analysis to datasets with thousands of variables. Proceedings of the 2015 SIAM International Conference on Data Mining, pp. 469-477.
Best Research Paper Honorable Mention Award
Chen, S., Martinez, A., & Webb, G. I. (2014). Highly Scalable Attribute Selection for AODE. Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 86-97.
Webb, G. I., & Vreeken, J. (2014). Efficient Discovery of the Most Interesting Associations. ACM Transactions on Knowledge Discovery from Data, 8(3), Art. no. 15.
Petitjean, F., Forestier, G., Webb, G. I., Nicholson, A., Chen, Y., & Keogh, E. (2014). Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification. Proceedings of the 14th IEEE International Conference on Data Mining, pp. 470-479.
ICDM 2023 10-year Highest Impact Paper Award
Zaidi, N., Carman, M., Cerquides, J., & Webb, G. I. (2014). Naive-Bayes Inspired Effective Pre-Conditioner for Speeding-up Logistic Regression. Proceedings of the 14th IEEE International Conference on Data Mining, pp. 1097-1102.
Provost, F., Webb, G. I., Bekkerman, R., Etzioni, O., Fayyad, U., & Perlich, C. (2014). A Data Scientist's Guide to Start-Ups. Big Data, 2(3), 117-128.
Petitjean, F., Allison, L., & Webb, G. I. (2014). A Statistically Efficient and Scalable Method for Log-Linear Analysis of High-Dimensional Data. Proceedings of the 14th IEEE International Conference on Data Mining, pp. 480-489.
One of nine papers invited to Knowledge and Information Systems journal ICDM-14 special issue
Webb, G. I. (2014). Contrary to Popular Belief Incremental Discretization can be Sound, Computationally Efficient and Extremely Useful for Streaming Data. Proceedings of the 14th IEEE International Conference on Data Mining, pp. 1031-1036.
Li, Y., Wang, M., Wang, H., Tan, H., Zhang, Z., Webb, G. I., & Song, J. (2014). Accurate in Silico Identification of Species-Specific Acetylation Sites by Integrating Protein Sequence-Derived and Functional Features. Scientific Reports, 4, Art. no. 5765.
Suraweera, P., Webb, G. I., Evans, I., & Wallace, M. (2013). Learning crew scheduling constraints from historical schedules. Transportation Research Part C: Emerging Technologies, 26, 214-232.
Petitjean, F., Webb, G. I., & Nicholson, A. E. (2013). Scaling log-linear analysis to high-dimensional data. Proceedings of the 13th IEEE International Conference on Data Mining, pp. 597-606.
Zaidi, N. A., Cerquides, J., Carman, M. J., & Webb, G. I. (2013). Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting. Journal of Machine Learning Research, 14, 1947-1988.
Zaidi, N., & Webb, G. I. (2013). Fast and Effective Single Pass Bayesian Learning. Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 149-160.
Provost, F., & Webb, G. I. (2013). Panel: a data scientist's guide to making money from start-ups. Proceedings of the 9th ACM SIGKDD International Conference on knowledge Discovery and Data Mining, pp. 1445-1445.
Song, J., Tan, H., Wang, M., Webb, G. I., & Akutsu, T. (2012). TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences. PLoS ONE, 7(2), Art. no. e30361.
Zheng, F., Webb, G. I., Suraweera, P., & Zhu, L. (2012). Subsumption Resolution: An Efficient and Effective Technique for Semi-Naive Bayesian Learning. Machine Learning, 87(1), 93-125.
Song, J., Tan, H., Perry, A. J., Akutsu, T., Webb, G. I., Whisstock, J. C., & Pike, R. N. (2012). PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites. PLoS ONE, 7(11), Art. no. e50300.
Salem, H., Suraweera, P., Webb, G. I., & Boughton, J. R. (2012). Techniques for Efficient Learning without Search. Proceedings of the 16th Pacific-Asia Conference, PAKDD 2012, Berlin/Heidelberg, pp. 50-61.
Martinez, A., Webb, G. I., Flores, M., & Gamez, J. (2012). Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers. Proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems, Berlin / Heidelberg, pp. 151-162.
Webb, G. I., Boughton, J., Zheng, F., Ting, K. M., & Salem, H. (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification. Machine Learning, 86(2), 233-272.
Mahmood, K., Webb, G. I., Song, J., Whisstock, J. C., & Konagurthu, A. S. (2012). Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs. Nucleic Acids Research, 40(6), Art. no. e44.
Ting, K. M., Wells, J., Tan, S., Teng, S., & Webb, G. I. (2011). Feature-subspace aggregating: Ensembles for stable and unstable learners. Machine Learning, 82(3), 375-397.
Song, J., Tan, H., Boyd, S. E., Shen, H., Mahmood, K., Webb, G. I., Akutsu, T., Whisstock, J. C., & Pike, R. N. (2011). Bioinformatic Approaches for Predicting Substrates of Proteases. Journal of Bioinformatics and Computational Biology, 9(1), 149-178.
Ng, N. M., Pierce, J. D., Webb, G. I., Ratnikov, B. I., Wijeyewickrema, L. C., Duncan, R. C., Robertson, A. L., Bottomley, S. P., Boyd, S. E., & Pike, R. N. (2011). Discovery of Amino Acid Motifs for Thrombin Cleavage and Validation Using a Model Substrate. Biochemistry, 50(48), 10499-10507.
Webb, G. I. (2011). Filtered-top-k Association Discovery. WIREs Data Mining and Knowledge Discovery, 1(3), 183-192.
Webb, G. I. (2010). Self-Sufficient Itemsets: An Approach to Screening Potentially Interesting Associations Between Items. ACM Transactions on Knowledge Discovery from Data, 4, Art. no. 3.
Song, J., Tan, H., Shen, H., Mahmood, K., Boyd, S. E., Webb, G. I., Akutsu, T., & Whisstock, J. C. (2010). Cascleave: Towards More Accurate Prediction of Caspase Substrate Cleavage Sites. Bioinformatics, 26(6), 752-760.
Mahmood, K., Konagurthu, A. S., Song, J., Buckle, A. M., Webb, G. I., & Whisstock, J. C. (2010). EGM: Encapsulated Gene-by-Gene Matching to Identify Gene Orthologs and Homologous Segments in Genomes. Bioinformatics, 26(17), 2076-2084.
Webb, G. I., Liu, B., Zhang, C., Gunopulos, D., & Wu, X. (Ed). (2010). ICDM 2010, The 10th IEEE International Conference on Data Mining. IEEE Computer Society.
Sammut, C., & Webb, G. I. (Ed). (2010). Encyclopedia of Machine Learning. Berlin: Springer.
Liu, B., Yang, Y., Webb, G. I., & Boughton, J. (2009). A Comparative Study of Bandwidth Choice in Kernel Density Estimation for Naive Bayesian Classification. Proceedings of the 13th Pacific-Asia Conference, PAKDD 2009, Berlin/Heidelberg, pp. 302-313.
Song, J., Tan, H., Mahmood, K., Law, R. H. P., Buckle, A. M., Webb, G. I., Akutsu, T., & Whisstock, J. C. (2009). Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only. PLoS ONE, 4(9), Art. no. e7072.
Ting, K. M., Wells, J. R., Tan, S. C., Teng, S. W., & Webb, G. I. (2009). FaSS: Ensembles for Stable Learners. Proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009, Berlin, pp. 364-374.
Novak, P., Lavrac, N., & Webb, G. I. (2009). Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining. Journal of Machine Learning Research, 10, 377-403.
Yang, Y., & Webb, G. I. (2009). Discretization for Naive-Bayes Learning: Managing Discretization Bias and Variance. Machine Learning, 74(1), 39-74.
Hui, B., Yang, Y., & Webb, G. I. (2009). Anytime Classification for a Pool of Instances. Machine Learning, 77(1), 61-102.
Webb, G. I. (2008). Layered Critical Values: A Powerful Direct-Adjustment Approach to Discovering Significant Patterns. Machine Learning, 71(2-3), 307-323.
Webb, G. I. (2008). Multi-Strategy Ensemble Learning, Ensembles of Bayesian Classifiers, and the Problem of False Discoveries. Proceedings of the Seventh Australasian Data Mining Conference (AusDM 2008), pp. 15.
Faux, N. G., Huttley, G. A., Mahmood, K., Webb, G. I., Garcia de la Banda, M., & Whisstock, J. C. (2007). RCPdb: An evolutionary classification and codon usage database for repeat-containing proteins. Genome Research, 17(1), 1118-1127.
Yang, Y., Webb, G. I., Cerquides, J., Korb, K., Boughton, J., & Ting, K-M. (2007). To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators. IEEE Transactions on Knowledge and Data Engineering, 19(12), 1652-1665.
Zheng, F., & Webb, G. I. (2007). Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators. Lecture Notes in Artificial Intelligence 4710: Proceedings of the 18th European Conference on Machine Learning (ECML'07), Berlin/Heidelberg, pp. 490-501.
Webb, G. I. (2007). Finding the Real Patterns (Extended Abstract). Lecture Notes in Computer Science Vol. 4426 : Advances in Knowledge Discovery and Data Mining Proceedings of the 11th Pacific-Asia Conference, PAKDD 2007, Berlin/Heidelberg, pp. 6.
Webb, G. I. (2007). Tenth Anniversary Edition Editorial. Data Mining and Knowledge Discovery, 15(1), 1-2.
Webb, G. I. (2007). Discovering Significant Patterns. Machine Learning, 68(1), 1-33.
Yang, Y., Webb, G. I., Korb, K., & Ting, K-M. (2007). Classifying under Computational Resource Constraints: Anytime Classification Using Probabilistic Estimators. Machine Learning, 69(1), 35-53.
Zheng, F., & Webb, G. I. (2006). Efficient Lazy Elimination for Averaged One-Dependence Estimators. ACM International Conference Proceeding Series, Vol. 148: The Proceedings of the Twenty-third International Conference on Machine Learning (ICML'06), New York, NY, pp. 1113-1120.
Butler, S., & Webb, G. I. (2006). Mining Group Differences. In Wang, J. (Ed.), In The Encyclopedia of Data Warehousing and Mining (, pp. 795-799). Hershey, PA: Idea Group Inc..
Webb, G. I. (2006). Anytime Learning and Classification for Online Applications. Advances in Intelligent IT: Proceedings of the Fourth International Conference on Active Media Technology (AMT'06). [Extended Abstract], Amsterdam, pp. 7-12.
Yang, Y., & Webb, G. I. (2006). Discretization for Data Mining. In Wang, J. (Ed.), In The Encyclopedia of Data Warehousing and Mining (, pp. 392-396). Hershey, PA: Idea Group Inc..
Yang, Y., Webb, G. I., Cerquides, J., Korb, K., Boughton, J., & Ting, K-M. (2006). To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. Lecture Notes in Computer Science 4212: Proceedings of the 17th European Conference on Machine Learning (ECML'06), Berlin/Heidelberg, pp. 533-544.
Huang, S., & Webb, G. I. (2006). Efficiently Identifying Exploratory Rules' Significance. LNAI State-of-the-Art Survey series, 'Data Mining: Theory, Methodology, Techniques, and Applications', Berlin/Heidelberg, pp. 64-77.
Lu, J., Yang, Y., & Webb, G. I. (2006). Incremental Discretization for Naive-Bayes Classifier. Lecture Notes in Computer Science 4093: Proceedings of the Second International Conference on Advanced Data Mining and Applications (ADMA 2006), Berlin, pp. 223-238.
Yang, Q., & Webb, G. I. (Ed). (2006). Lecture Notes in Artificial Intelligence 4099: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2006). Berlin: Springer.
Webb, G. I., & Brain, D. (2006). Generality is Predictive of Prediction Accuracy. LNAI State-of-the-Art Survey series, 'Data Mining: Theory, Methodology, Techniques, and Applications', Berlin/Heidelberg, pp. 1-13.
Webb, G. I. (2006). Discovering Significant Rules. Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006), New York, pp. 434-443.
Siu, K. K. W., Butler, S. M., Beveridge, T., Gillam, J. E., Hall, C. J., Kaye, A. H., Lewis, R. A., Mannan, K., McLoughlin, G., Pearson, S., Round, A. R., E., S., Webb, G. I., & Wilkinson, S. J. (2005). Identifying markers of pathology in SAXS data of malignant tissues of the brain. Nuclear Instruments and Methods in Physics Research A, 548, 140-146.
Yang, Y., Korb, K., Ting, K-M., & Webb, G. I. (2005). Ensemble Selection for SuperParent-One-Dependence Estimators. Lecture Notes in Computer Science 3809: Advances in Artificial Intelligence, Proceedings of the 18th Australian Joint Conference on Artificial Intelligence (AI 2005), Berlin/Heidelberg, pp. 102-111.
Huang, S., & Webb, G. I. (2005). Pruning Derivative Partial Rules During Impact Rule Discovery. Lecture Notes in Computer Science Vol. 3518: Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2005), Berlin/Heidelberg, pp. 71-80.
Webb, G. I., & Zhang, S. (2005). k-Optimal-Rule-Discovery. Data Mining and Knowledge Discovery, 10(1), 39-79.
Webb, G. I., & Ting, K. M. (2005). On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Machine Learning, 58(1), 25-32.
Webb, G. I., Boughton, J., & Wang, Z. (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning, 58(1), 5-24.
Zheng, F., & I., W. G. (2005). A Comparative Study of Semi-naive Bayes Methods in Classification Learning. Proceedings of the Fourth Australasian Data Mining Conference (AusDM05), Sydney, pp. 141-156.
Huang, S., & Webb, G. I. (2005). Discarding Insignificant Rules During Impact Rule Discovery in Large, Dense Databases. Proceedings of the Fifth SIAM International Conference on Data Mining (SDM'05) [short paper], Philadelphia, PA, pp. 541-545.
Webb, G. I. (2005). K-Optimal Pattern Discovery: An Efficient and Effective Approach to Exploratory Data Mining. Lecture Notes in Computer Science 3809: Advances in Artificial Intelligence, Proceedings of the 18th Australian Joint Conference on Artificial Intelligence (AI 2005)[Extended Abstract], Berlin/Heidelberg, pp. 1-2.
Yang, Y., Webb, G. I., & Wu, X. (2005). Discretization Methods. In Maimon, O., & Rokach, L. (Eds.), In The Data Mining and Knowledge Discovery Handbook (, pp. 113-130). Berlin: Springer.
Webb, G. I., & Yu, X. (Ed). (2004). Lecture Notes in Computer Science 3339: Proceedings of the 17th Australian Joint Conference on Artificial Intelligence (AI 2004). Berlin: Springer.
Webb, G. I., & Zheng, Z. (2004). Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques. IEEE Transactions on Knowledge and Data Engineering, 16(8), 980-991.
Webb, G. I., & Conilione, P. (2004). Estimating bias and variance from data. Unpublished manuscript.
Newlands, D. A., & Webb, G. I. (2004). Convex Hulls as an Hypothesis Language Bias. Proceedings of the Fourth International Conference on Data Mining (DATA MINING IV), Southampton, UK, pp. 285-294.
Newlands, D. A., & Webb, G. I. (2004). Alternative Strategies for Decision List Construction. Proceedings of the Fourth International Conference on Data Mining (DATA MINING IV), Southampton, UK, pp. 265-273.
Thiruvady, D. R., & Webb, G. I. (2004). Mining Negative Rules using GRD. Lecture Notes in Computer Science Vol. 3056: Proceedings of the Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 04) [Short Paper], Berlin/Heidelberg, pp. 161-165.
Wang, Z., Webb, G. I., & Zheng, F. (2004). Selective Augmented Bayesian Network Classifiers Based on Rough Set Theory. Lecture Notes in Computer Science Vol. 3056: Proceedings of the Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 04), Berlin/Heidelberg, pp. 319-328.
Webb, G. I., Butler, S., & Newlands, D. (2003). On Detecting Differences Between Groups. Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003), New York, pp. 256-265.
Zhang, C., Zhang, S., & Webb, G. I. (2003). Identifying Approximate Itemsets of Interest In Large Databases. Applied Intelligence, 18, 91-104.
Rolfe, B., Frayman, Y., Webb, G. I., & Hodgson, P. (2003). Analysis of Stamping Production Data with View Towards Quality Management. Proceedings of the 9th International Conference on Manufacturing Excellence (ICME 03).
Webb, G. I. (2003). Association Rules. In Ye, N. (Ed.), In The Handbook of Data Mining, Chapter 2 (pp. 25-39). Lawrence Erlbaum Associates.
Wang, Z., Webb, G. I., & Zheng, F. (2003). Adjusting Dependence Relations for Semi-Lazy TAN Classifiers. Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03), Berlin/Heidelberg, pp. 453-465.
Rolfe, B., Hodgson, P., & Webb, G. I. (2003). Improving the Prediction of the Roll Separating Force in a Hot Steel Finishing Mill. Intelligence in a Small World - Nanomaterials for the 21st Century. Selected Papers from IPMM-2003, Boca Raton, Florida.
Webb, G. I. (2003). Preliminary Investigations into Statistically Valid Exploratory Rule Discovery. Proceedings of the Second Australasian Data Mining Conference (AusDM03), Sydney, pp. 1-9.
Butler, S. M., Webb, G. I., & Lewis, R. A. (2003). A Case Study in Feature Invention for Breast Cancer Diagnosis Using X-Ray Scatter Images. Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03), Berlin/Heidelberg, pp. 677-685.
Shi, H., Wang, Z., Webb, G. I., & Huang, H. (2003). A New Restricted Bayesian Network Classifier. Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'03), Berlin/Heidelberg, pp. 265-270.
Yang, Y., & Webb, G. I. (2003). Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers. Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'03), Berlin/Heidelberg, pp. 501-512.
Yang, Y., & Webb, G. I. (2003). On Why Discretization Works for Naive-Bayes Classifiers. Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03), Berlin/Heidelberg, pp. 440-452.
Pearce, J., Webb, G. I., Shaw, R., & Garner, B. (2002). A Systemic Approach to the Database Marketing Process. Proceedings of the Australian and New Zealand Marketing Academy Conference (ANZMAC 02), Geelong, Victoria, pp. 2941-2948.
Pearce, J., Webb, G. I., Shaw, R., & Garner, B. (2002). A Framework for Experimentation and Self Learning in Continuous Database Marketing. Proceedings of the IEEE International Conference on Data Mining (ICDM-2002), Los Alamitos, CA, pp. 490-497.
Webb, G. I. (2002). Integrating Machine Learning with Knowledge Acquisition. In Leondes, C. T. (Ed.), In Expert Systems (, Vol. 3, pp. 937-959). San Diego, CA: Academic Press.
Yang, Y., & Webb, G. I. (2002). Non-Disjoint Discretization for Naive-Bayes Classifiers. Proceedings of the Nineteenth International Conference on Machine Learning (ICML '02), San Francisco, pp. 666-673.
Frayman, Y., Rolfe, B., Hodgson, P., & Webb, G. I. (2002). Predicting The Rolling Force in Hot Steel Rolling Mill using an Ensemble Model. Proceedings of the Second IASTED International Conference on Artificial Intelligence and Applications (AIA '02), Calgary, Canada, pp. 143-148.
Frayman, Y., Rolfe, B., & Webb, G. I. (2002). Improving an Inverse Model of Sheet Metal Forming by Neural Network Based Regression. Proceedings of the Design Engineering Technical Conferences and Computer and Information in Engineering Conference (DETC'02/ASME 2002), New York, pp. 1-8.
Frayman, Y., Rolfe, B., & Webb, G. I. (2002). Solving Regression Problems using Competitive Ensemble Models. Lecture Notes in Computer Science Vol. 2557: Proceedings of the 15th Australian Joint Conference on Artificial Intelligence (AI 02), Berlin/Heidelberg, pp. 511-522.
Rolfe, B., Frayman, Y., Hodgson, P., & Webb, G. I. (2002). Fault Detection in a Cold Forging Process Through Feature Extraction with a Neural Network. Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2002), Calgary, Canada, pp. 155-159.
Yang, Y., & Webb, G. I. (2002). A Comparative Study of Discretization Methods for Naive-Bayes Classifiers. Proceedings of the 2002 Pacific Rim Knowledge Acquisition Workshop (PKAW'02), Tokyo, pp. 159-173.
Webb, G. I., Boughton, J., & Wang, Z. (2002). Averaged One-Dependence Estimators: Preliminary Results. Proceedings of the First Australasian Data Mining Workshop (AusDM02), Sydney, pp. 65-73.
Webb, G. I., & Brain, D. (2002). Generality is Predictive of Prediction Accuracy. Proceedings of the 2002 Pacific Rim Knowledge Acquisition Workshop (PKAW'02), Tokyo, pp. 117-130.
Wang, Z., & Webb, G. I. (2002). A Heuristic Lazy Bayesian Rules Algorithm. Proceedings of the First Australasian Data Mining Workshop (AusDM02), Sydney, pp. 57-63.
Webb, G. I., & Zhang, S. (2002). Removing Trivial Associations in Association Rule Discovery. Proceedings of the First International NAISO Congress on Autonomous Intelligent Systems (ICAIS 2002), Canada/The Netherlands.
Brain, D., & Webb, G. I. (2002). The Need for Low Bias Algorithms in Classification Learning From Large Data Sets. Lecture Notes in Computer Science 2431: Principles of Data Mining and Knowledge Discovery: Proceedings of the Sixth European Conference (PKDD 2002), Berlin/Heidelberg, pp. 62-73.
Wang, Z., & Webb, G. I. (2002). Comparison of Lazy Bayesian Rule Learning and Tree-Augmented Bayesian Learning. Proceedings of the IEEE International Conference on Data Mining (ICDM-2002), Los Alamitos, CA, pp. 775-778.
Webb, G. I. (2001). Discovering Associations with Numeric Variables. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)[short paper], New York, pp. 383-388.
Webb, G. I., Pazzani, M. J., & Billsus, D. (2001). Machine learning for user modeling. User Modeling and User-Adapted Interaction, 11, 19-20.
Webb, G. I. (2001). Candidate Elimination Criteria for Lazy Bayesian Rules. Lecture Notes in Computer Science Vol. 2256: Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01), Berlin/Heidelberg, pp. 545-556.
Webb, G. I., & Zhang, S. (2001). Further Pruning for Efficient Association Rule Discovery. Lecture Notes in Computer Science Vol. 2256: Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01), Berlin, pp. 605-618.
Wang, Z., Webb, G. I., & Dai, H. (2001). Implementation of Lazy Bayesian Rules in the Weka System. Software Technology Catering for 21st Century: Proceedings of the International Symposium on Future Software Technology (ISFST2001), Tokyo, pp. 204-208.
Yang, Y., & Webb, G. I. (2001). Proportional K-Interval Discretization for Naive-Bayes Classifiers. Lecture Notes in Computer Science 2167: Proceedings of the 12th European Conference on Machine Learning (ECML'01), Berlin/Heidelberg, pp. 564-575.
Webb, G. I. (2000). Efficient Search for Association Rules. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000), New York, pp. 99-107.
Zheng, Z., & Webb, G. I. (2000). Lazy Learning of Bayesian Rules. Machine Learning, 41(1), 53-84.
Webb, G. I. (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning, 40(2), 159-196.
Smith, P. A., & Webb, G. I. (2000). The Efficacy of a Low-Level Program Visualization Tool for Teaching Programming Concepts to Novice C Programmers. Journal of Educational Computing Research, 22(2), 187-215.
Zheng, Z., Webb, G. I., & Ting, K. M. (1999). Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), San Francisco, pp. 493-502.
Webb, G. I. (1999). Decision Tree Grafting From The All Tests But One Partition. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI 99), San Francisco, pp. 702-707.
Ting, K. M., Zheng, Z., & Webb, G. I. (1999). Learning Lazy Rules to Improve the Performance of Classifiers. Proceedings of the Nineteenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (ES'99), New York, pp. 122-131.
Newlands, D., & Webb, G. I. (1999). Convex Hulls in Concept Induction. Lecture Notes in Computer Science 1574: Methodologies for Knowledge Discovery and Data Mining - Proceedings of the Third Pacific-Asia Conference (PAKDD'99), Berlin/Heidelberg, pp. 306-316.
Smith, P. A., & Webb, G. I. (1999). Evaluation of Low-Level Program Visualisation for Teaching Novice C Programmers. Proceedings of the Seventh International Conference on Computers in Education (ICCE '99), Amsterdam, pp. 385-392.
Zheng, Z., & Webb, G. I. (1999). Stochastic Attribute Selection Committees with Multiple Boosting: Learning More Accurate and More Stable Classifier Committees. Lecture Notes in Computer Science 1574: Methodologies for Knowledge Discovery and Data Mining - Proceedings of the Third Pacific-Asia Conference (PAKDD'99), Berlin/Heidelberg, pp. 123-132.
Webb, G. I., Wells, J., & Zheng, Z. (1999). An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition. Machine Learning, 35(1), 5-24.
Chiu, B. C., & Webb, G. I. (1999). Dual-Model: An Architecture for Utilizing Temporal Information in Student Modeling. Proceedings of the Seventh International Conference on Computers in Education (ICCE '99), Amsterdam, pp. 111-118.
Brain, D., & Webb, G. I. (1999). On The Effect of Data Set Size on Bias And Variance in Classification Learning. Proceedings of the Fourth Australian Knowledge Acquisition Workshop (AKAW-99), Sydney, pp. 117-128.
Webb, G. I., & Pazzani, M. (1998). Adjusted Probability Naive Bayesian Induction. Lecture Notes in Computer Science Vol. 1502: Advanced Topics in Artificial Intelligence, Selected Papers from the Eleventh Australian Joint Conference on Artificial Intelligence (AI '98), Berlin, pp. 285-295.
Webb, G. I. (1998). The Problem of Missing Values in Decision Tree Grafting. Lecture Notes in Computer Science Vol. 1502: Advanced Topics in Artificial Intelligence, Selected Papers from the Eleventh Australian Joint Conference on Artificial Intelligence (AI '98), Berlin, pp. 273-283.
Zheng, Z., & Webb, G. I. (1998). Multiple Boosting: A Combination of Boosting and Bagging. Proceedings of the 1998 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'98), pp. 1133-1140.
Zheng, Z., & Webb, G. I. (1998). Stochastic Attribute Selection Committees. Lecture Notes in Computer Science Vol. 1502: Advanced Topics in Artificial Intelligence, Selected Papers from the Eleventh Australian Joint Conference on Artificial Intelligence (AI '98), Berlin, pp. 321-332.
Webb, G. I. (1998). Preface to UMUAI Special Issue on Machine Learning for User Modeling. User Modeling and User-Adapted Interaction, 8(1), 1-3.
Zheng, Z., Webb, G. I., & Ting, K. M. (1998). Integrating Boosting and Stochastic Attribute Selection Committees for Further Improving The Performance of Decision Tree Learning. Proceedings of the Tenth IEEE International Conference on Tools with Artificial Intelligence (ICTAI-98), Los Alamitos, CA, pp. 216-223.
Webb, G. I., & Kuzmycz, M. (1998). Evaluation Of Data Aging: A Technique For Discounting Old Data During Student Modeling. Lecture Notes in Computer Science Vol. 1452: Proceedings of the Fourth International Conference on Intelligent Tutoring Systems (ITS '98), Berlin, pp. 384-393.
Viswanathan, M., & Webb, G. I. (1998). Classification Learning Using All Rules. Lecture Notes in Computer Science 1398: Proceedings of the Tenth European Conference on Machine Learning (ECML'98), Berlin/Heidelberg, pp. 149-159.
Smith, P. A., & Webb, G. I. (1998). Overview of a Low-Level Program Visualisation Tool for Novice Programmers. Proceedings of the Sixth International Conference on Computers in Education (ICCE '98), Berlin, pp. 213-216.
Chiu, B. C., & Webb, G. I. (1998). Using Decision Trees For Agent Modelling: Improving Prediction Performance. User Modeling and User-Adapted Interaction, 8(1-2), 131-152.
Chiu, B. C., Webb, G. I., & Zheng, Z. (1997). Using Decision Trees for Agent Modelling: A Study on Resolving Conflicting Predictions. Lecture Notes in Computer Science Vol. 1342: Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence (AI'97), Berlin, pp. 349-358.
Chiu, B. C., Webb, G. I., & Kuzmycz, M. (1997). A Comparison of First-Order and Zeroth-Order Induction for Input-Output Agent Modelling. Proceedings of the Sixth International Conference on User Modeling (UM'97), New York/Vienna, pp. 347-358.
Chiu, B. C., & Webb, G. I. (1997). Using C4.5 as an Induction Engine for Agent Modeling: An Experiment of Optimisation. Proceedings (on-line) of The First Machine Learning for User Modeling Workshop (UM'97).
Webb, G. I. (1997). Decision Tree Grafting. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 97), San Francisco, pp. 846-851.
Webb, G. I., Chiu, B. C., & Kuzmycz, M. (1997). Comparative Evaluation of Alternative Induction Engines for Feature Based Modelling. International Journal of Artificial Intelligence in Education, 8, 97-115.
Webb, G. I., & Wells, J. (1996). An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition Through Direct Interaction with Domain Experts. Proceedings of the 1996 Pacific Knowledge Acquisition Workshop (PKAW'96), Sydney, pp. 170-189.
Webb, G. I. (1996). Inclusive Pruning: A New Class of Pruning Rule for Unordered Search and its Application to Classification Learning. Australian Computer Science Communications Vol. 18 (1): Proceedings of the Nineteenth Australasian Computer Science Conference (ACSC'96), Melbourne, pp. 1-10.
Webb, G. I., & Kuzmycz, M. (1996). Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents Competencies. User Modeling and User-Adapted Interaction, 5(2), 117-150.
Webb, G. I. (1996). Cost Sensitive Specialisation. Lecture Notes in Computer Science Vol. 1114. Topics in Artificial Intelligence: Proceedings of the Fourth Pacific Rim International Conference on Artificial Intelligence (PRICAI'96), Berlin/Heidelberg, pp. 23-34.
Webb, G. I. (1996). Integrating Machine Learning With Knowledge Acquisition Through Direct Interaction With Domain Experts. Knowledge-Based Systems, 9, 253-266.
Webb, G. I. (1996). A Heuristic Covering Algorithm Outperforms Learning All Rules. Proceedings of Information, Statistics and Induction in Science (ISIS '96), Singapore, pp. 20-30.
Webb, G. I. (1996). Further Experimental Evidence Against The Utility Of Occam's Razor. Journal of Artificial Intelligence Research, 4, 397-417.
Smith, P. A., & Webb, G. I. (1995). Reinforcing a Generic Computer Model for Novice Programmers. Proceedings of the Seventh Australian Society for Computers in Learning in Tertiary Education Conference (ASCILITE '95), Melbourne.
Webb, G. I. (1995). OPUS: An Efficient Admissible Algorithm For Unordered Search. Journal of Artificial Intelligence Research, 3, 431-465.
Smith, P. A., & Webb, G. I. (1995). Transparency Debugging with Explanations for Novice Programmers. Proceedings of the Second International Workshop on Automated and Algorithmic Debugging (AADEBUG'95).
Newlands, D., & Webb, G. I. (1995). Polygonal Inductive Generalisation System. Proceedings of the Eighth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE '95), Newark, NJ, USA, pp. 587-592.
Webb, G. I., & Wells, J. (1995). Recent Progress in Machine-Expert Collaboration for Knowledge Acquisition. Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence (AI'95), Singapore, pp. 291-298.
Webb, G. I. (1994). Generality Is More Significant Then Complexity: Toward An Alternative To Occams Razor. Artificial Intelligence: Sowing the Seeds for the Future, Proceedings of Seventh Australian Joint Conference on Artificial Intelligence (AI'94), Singapore, pp. 60-67.
Yip, S., & Webb, G. I. (1994). Incorporating Canonical Discriminate Attributes in Classification Learning. Proceedings of the Tenth Biennial Canadian Artificial Intelligence Conference(AI-94), San Francisco, pp. 63-70.
Yip, S., & Webb, G. I. (1994). Empirical Function Attribute Construction in Classification Learning. Artificial Intelligence: Sowing the Seeds for the Future, Proceedings of Seventh Australian Joint Conference on Artificial Intelligence (AI'94), Singapore, pp. 29-36.
Webb, G. I. (1994). Recent Progress in Learning Decision Lists by Prepending Inferred Rules. Proceedings of the Second Singapore International Conference on Intelligent Systems (SPICIS-94), Singapore, pp. 280-285.
Webb, G. I. (1993). Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents Competency. Proceedings of the 1993 World Conference on Artificial Intelligence in Education (AI-ED'93), Charlottesville, VA, pp. 497-504.
Webb, G. I. (1993). DLGref2: Techniques for Inductive Rule Refinement. Proceedings of the 1993 IJCAI Workshop W16: Machine Learning and Knowledge Acquisition, pp. 236-252.
Webb, G. I., & Brkic, N. (1993). Learning Decision Lists by Prepending Inferred Rules. Proceedings of the AI 93 Workshop on Machine Learning and Hybrid Systems, pp. 6-10.
Webb, G. I. (1993). Control, Capabilities and Communication: Three Key Issues for Machine-Expert Collaborative Knowledge Acquisition. Proceedings (Complement) of the Seventh European Workshop on Knowledge Acquisition for Knowledge-based Systems (EWKA'93), pp. 263-275.
Webb, G. I. (1993). Systematic Search for Categorical Attribute-Value Data-Driven Machine Learning. Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence (AI'93), Singapore, pp. 342-347.
Kuzmycz, M., & Webb, G. I. (1992). Evaluation of Feature Based Modelling in Subtraction. Lecture Notes in Computer Science Vol. 608: Proceedings of the Second International Conference on Intelligent Tutoring Systems (ITS'92), Berlin, pp. 269-276.
Smith, P. A., & Webb, G. I. (1992). Recent progress in the Development of a Debugging Assistant for Computer Programs. A Future Promised: Proceedings of the Fifth Australian Society for Computers in Learning in Tertiary Education Conference (ASCILITE '92), pp. 351-356.
Yip, S., & Webb, G. I. (1992). Discriminate Attribute Finding in Classification Learning. Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence (AI'92), Singapore, pp. 374-379.
Webb, G. I., & Agar, J. (1992). Inducing Diagnostic Rules For Glomerular Disease With The DLG Machine Learning Algorithm. Artificial Intelligence in Medicine, 4(6), 419-430.
Yip, S., & Webb, G. I. (1992). Function Finding in Classification Learning. Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence (PRICAI '92), Berlin, pp. 555-561.
Agar, J., & Webb, G. I. (1992). Application Of Machine Learning To A Renal Biopsy Data-Base. Nephrology, Dialysis and Transplantation, 7, 472-478.
Webb, G. I. (1992). Man-Machine Collaboration for Knowledge Acquisition. Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence (AI'92), Singapore, pp. 329-334.
Kuzmycz, M., & Webb, G. I. (1991). Modelling Elementary Subtraction: Intelligent Warfare Against Bugs. Simulation & Academic Gaming in Tertiary Education, The Proceedings of the Eighth Annual Conference of ASCILITE (ASCILITE '91), Launceston, pp. 367-376.
Webb, G. I. (1991). Data Driven Inductive Refinement of Production Rules. Proceedings of the First Australian Workshop on Knowledge Acquisition for Knowledge-Based Systems (AKAW '91), Sydney, pp. 44-52.
Webb, G. I. (1991). Einstein: An Interactive Inductive Knowledge-Acquisition Tool. Proceedings of the Sixth Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, pp. (36)1-16.
Webb, G. I., & Agar, J. (1991). The Application of Machine Learning to the Diagnosis of Glomerular Disease. Proceedings of the IJCAI Workshop W.15: Representing Knowledge in Medical Decision Support Systems, pp. 8.1-8.8.
Smith, P. A., & Webb, G. I. (1991). Debugging Using Partial Models. Simulation & Academic Gaming in Tertiary Education, The Proceedings of the Eighth Annual Conference of ASCILITE (ASCILITE '91), Launceston, pp. 581-590.
Webb, G. I. (1991). Inside the Unification Tutor: The Architecture of an Intelligent Educational System. Simulation & Academic Gaming in Tertiary Education, The Proceedings of the Eighth Annual Conference of ASCILITE (ASCILITE '91), Launceston, pp. 677-684.
Webb, G. I. (1991). An Attribute-Value Machine Learning Approach To Student Modelling. Proceedings of the IJCAI Workshop W.4: Agent Modelling for Intelligent Interaction, pp. 128-136.
Sanzogni, L., Surruwerra, F., & Webb, G. I. (1990). Improving the Efficiency of Rule Based Expert Systems by Rule Activation. Journal of Experimental and Theoretical Artificial Intelligence, 2, 369-380.
Webb, G. I., Cumming, G., Richards, T., & Yum, K-K. (1990). Educational Evaluation of Feature Based Modelling in a Problem Solving Domain. Proceedings of the IFIP TC3 International Conference on Advanced Research on Computers in Education (ARCE'90), Amsterdam, pp. 101-108.
Webb, G. I. (1990). Rule Optimisation and Theory Optimisation: Heuristic Search Strategies for Data-Driven Machine Learning. Proceedings of the First Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW'90), Tokyo, pp. 219-232.
Webb, G. I., Cumming, G., Richards, T., & Yum, K-K. (1989). The Unification Tutor: An Intelligent Educational System in the Classroom. Proceedings of the Seventh Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE '89), Gold Coast, pp. 408-420.
Webb, G. I. (1989). A Machine Learning Approach to Student Modelling. Proceedings of the Third Australian Joint Conference on Artificial Intelligence (AI 89), pp. 195-205.
Webb, G. I. (1989). Courseware Abstraction: Reducing Development Costs While Producing Qualitative Improvements in CAL. Journal of Computer Assisted Learning, 5, 103-113.
Richards, T., Webb, G. I., & Craske, N. (1988). Object-oriented Control for Intelligent Computer Assisted Learning Systems. Proceedings of the IFIP TC3 Working Conference on Artificial Intelligence Tools in Education, North-Holland, Amsterdam, pp. 203-219.
Webb, G. I. (1988). Cognitive Diagnosis Using Student Attributions. Computers in Learning in Tertiary education: Proceedings of the Sixth Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE-88), pp. 502-514.
Webb, G. I. (1988). A Knowledge-Based Approach To Computer-Aided Learning. International Journal of Man-Machine Studies, 29, 257-285.
Webb, G. I. (1988). Techniques for Efficient Empirical Induction. Lecture Notes in Artificial Intelligence Vol. 406: Proceedings of the Second Australian Joint Conference on Artificial Intelligence (AI'88), Berlin, pp. 225-239.
Webb, G. I. (1987). Generative CAL and Courseware Abstraction. Using computers intelligently in Tertiary Education: Proceedings of the Fifth Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE-87), pp. 257-285.
Webb, G. I. (1987). Domain and Tutoring Knowledge in Computer-Aided Learning. Proceedings of the First Australian Joint Conference on Artificial Intelligence (AI'87), Sydney, pp. 488-502.
Webb, G. I. (1986). Knowledge Based Flow of Control in Computer-Aided Learning. Proceedings of the First Australian Artificial Intelligence Congress (1AAIC'86), pp. B: 1-7.
Webb, G. I. (1986). The Domain-Analysis Based Instruction System. Proceedings of the Fourth Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'86), Adelaide, pp. 295-302.
Webb, G. I. (1985). Student Control Under the Feature-Network Based Courseware Design Methodology. Student Control of Learning: Proceedings of the Third Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'85), Melbourne, pp. 27-34.
Richards, T., & Webb, G. I. (1985). ECCLES An Expert System for CAL. Proceedings of the Tenth Western Educational Computing Conference (WECC'85), North Hollywood, CA, pp. 151-157.
Webb, G. I. (1984). A Methodology for Intermediate Level Knowledge Representation in CAL. Proceedings of the Second Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'84),, Brisbane, pp. 288-303.
Richards, T., Webb, G. I., & Bodnar, S. (1984). ECCLES An Intelligent C.A.L. System. Proceedings of the Second Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'84), Brisbane, pp. 232-235.
Richards, T., Hooke, R., & Webb, G. I. (1983). Automatic Authoring of Complex and Analytical Question-Answer Lessons. Proceedings of the First Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'83), Brisbane, pp. 282-293.
Webb, G. I. (1983). A Computer Based Language Instructional Expert. Proceedings of the First Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'83), Brisbane, pp. 391-402.