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. (in press). The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery. British Journal of Pharmacology.
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 Comput. Surv..
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.
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.
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.
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.
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.
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.
Zhang, H., Jiang, L., & Webb, G. I. (2023). Rigorous non-disjoint discretization for naive Bayes. Pattern Recognition, 140, Art. no. 109554.
Herrmann, M., & Webb, G. I. (2023). Amercing: An Intuitive and Effective Constraint for Dynamic Time Warping. Pattern Recognition, 137, Art. no. 109333.
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.
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.
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.
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.
Pialla, G., Fawaz, H. I., 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.
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).
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. 2023 IEEE International Conference on Data Mining (ICDM), pp. 239-248.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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., Fawaz, H. I., 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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, 2023
Krempl, G., Hofer, V., Webb, G., & Hullermeier, E. (2021). Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372) Schloss Dagstuhl - Leibniz-Zentrum fur Informatik.
Tan, C. W., Bergmeir, C., Petitjean, F., & Webb, G. I. (2021). Time series extrinsic regression. Data Mining and Knowledge Discovery, 35(3), 1032-1060.
Webb, G. I., & Petitjean, F. (2021). Tight lower bounds for Dynamic Time Warping. Pattern Recognition, 115, Art. no. 107895.
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.
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.
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 - 2023
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
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.
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.
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.
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.
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.
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.
Zaidi, N. A., Du, Y., & Webb, G. I. (2020). On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers. IEEE Access, 8, 198856-198871.
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.
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.
Phung, D., Webb, G. I., & Sammut, C. (Ed). (2020). Encyclopedia of Machine Learning and Data Science. Springer US.
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.
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
Fawaz, H. I., 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, 2023
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.
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
Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naive Bayes algorithm. Knowledge-Based Systems, 192, Art. no. 105361.
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.
Hamalainen, W., & Webb, G. I. (2019). A tutorial on statistically sound pattern discovery. Data Mining and Knowledge Discovery, 33(2), 325-377.
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.
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.
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.
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
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.
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.
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.
Yu, H., & Webb, G. I. (2019). Adaptive Online Extreme Learning Machine by Regulating Forgetting Factor by Concept Drift Map. Neurocomputing, 343, 141-153.
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 - 2023
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.
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.
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.
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.
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.
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., 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.
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.
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).
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.
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.
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.
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
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.
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 - 2023
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.
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.
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.
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.
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
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.
Sammut, C., & Webb, G. I. (Ed). (2017). Encyclopedia of Machine Learning and Data Mining. Berlin: Springer.
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.
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.
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.
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, 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zhang, A., Shi, W., & Webb, G. I. (2016). Mining significant association rules from uncertain data. Data Mining and Knowledge Discovery, 30(4), 928-963.
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
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.
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.
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.
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.
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.
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., 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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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. (2011). Filtered-top-k Association Discovery. WIREs Data Mining and Knowledge Discovery, 1(3), 183-192.
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.
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.
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.
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.
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.
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.
Sammut, C., & Webb, G. I. (Ed). (2010). Encyclopedia of Machine Learning. Berlin: Springer.
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.
Yang, Y., & Webb, G. I. (2009). Discretization for Naive-Bayes Learning: Managing Discretization Bias and Variance. Machine Learning, 74(1), 39-74.
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.
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.
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.
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). 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.
Webb, G. I. (2008). Layered Critical Values: A Powerful Direct-Adjustment Approach to Discovering Significant Patterns. Machine Learning, 71(2-3), 307-323.
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.
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.
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.
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.
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.
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..
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.
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.
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.
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.
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.
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.
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.
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.
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..
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., 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. (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., 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.
Webb, G. I., & Zhang, S. (2005). k-Optimal-Rule-Discovery. Data Mining and Knowledge Discovery, 10(1), 39-79.
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.
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.
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.
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., & Zheng, Z. (2004). Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques. IEEE Transactions on Knowledge and Data Engineering, 16(8), 980-991.
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.
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.
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., & 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., & Conilione, P. (2004). Estimating bias and variance from data. Unpublished manuscript.
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.
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).
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.
Webb, G. I. (2003). Association Rules. In Ye, N. (Ed.), In The Handbook of Data Mining, Chapter 2 (pp. 25-39). Lawrence Erlbaum Associates.
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.
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.
Zhang, C., Zhang, S., & Webb, G. I. (2003). Identifying Approximate Itemsets of Interest In Large Databases. Applied Intelligence, 18, 91-104.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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., & Brain, D. (2002). Generality is Predictive of Prediction Accuracy. Proceedings of the 2002 Pacific Rim Knowledge Acquisition Workshop (PKAW'02), Tokyo, pp. 117-130.
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.
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.
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.
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.
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.
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.
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., Pazzani, M. J., & Billsus, D. (2001). Machine learning for user modeling. User Modeling and User-Adapted Interaction, 11, 19-20.
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. (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.
Zheng, Z., & Webb, G. I. (2000). Lazy Learning of Bayesian Rules. Machine Learning, 41(1), 53-84.
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.
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.
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.
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., 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.
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.
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.
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.
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.
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.
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.
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). Preface to UMUAI Special Issue on Machine Learning for User Modeling. User Modeling and User-Adapted Interaction, 8(1), 1-3.
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.
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.
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). 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.
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.
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.
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. (1996). Further Experimental Evidence Against The Utility Of Occam's Razor. Journal of Artificial Intelligence Research, 4, 397-417.
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. (1996). Integrating Machine Learning With Knowledge Acquisition Through Direct Interaction With Domain Experts. Knowledge-Based Systems, 9, 253-266.
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., & 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). A Heuristic Covering Algorithm Outperforms Learning All Rules. Proceedings of Information, Statistics and Induction in Science (ISIS '96), Singapore, pp. 20-30.
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. (1995). OPUS: An Efficient Admissible Algorithm For Unordered Search. Journal of Artificial Intelligence Research, 3, 431-465.
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.
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.
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).
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. (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). 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.
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.
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). 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.
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). DLGref2: Techniques for Inductive Rule Refinement. Proceedings of the 1993 IJCAI Workshop W16: Machine Learning and Knowledge Acquisition, pp. 236-252.
Agar, J., & Webb, G. I. (1992). Application Of Machine Learning To A Renal Biopsy Data-Base. Nephrology, Dialysis and Transplantation, 7, 472-478.
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.
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.
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.
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). Discriminate Attribute Finding in Classification Learning. Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence (AI'92), Singapore, pp. 374-379.
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. (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). 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.
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., & 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.
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. (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.
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.
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. (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.
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.
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.
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. (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.
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.