Scientific applications of the AODE machine learning algorithm

In addition to its many commercial and educational applications, AODE has been used for machine learning and data mining in a variety of scientific applications.  The following are some publications that report research that uses AODE.

  1. Affendey, L.S., Paris, I.H.M. Mustapha, N. Sulaiman, M.N., Muda, Z.: Ranking of influencing factors in predicting students academic performance. Inform. Technol. J., 9 (2010) 832-837.
  2. Baig, Z.A., Shaheen, A.S., AbdelAal, R.: An AODE-based intrusion detection system for computer networks. In Proceedings of the 2011 World Congress on Internet Security (WorldCIS), 2011, 28-35.
  3. Balaniuk, R., Antonio do Prado, H., da Veiga Guadagnin, R., Ferneda, E., Cobbe, P.: Predicting evasion candidates in higher education institutions. In Proceedings First International Conference on Model and Data Engineering, Obidos, Portugal (2011) 143-151.
  4. Biemann, C. Co-occurrence cluster features for lexical substitutions in context. Proceedings of TextGraphs-5-2010 Workshop on Graph-based Methods for Natural Language Processing, 2010, 55-59
  5. Biemann, C. (2013). Creating a system for lexical substitutions from scratch using crowdsourcing. Language Resources and Evaluation, 1-26.
  6. Biemann, C.: Word Sense Induction and Disambiguation. Springer-Verlag, Berlin. (2012).
  7. Birzele, F., Kramer, S.: A new representation for protein secondary structure prediction based on frequent patterns. Bioinformatics 22(21) (2006) 2628–2634.
  8. Cagliero, L., Cerquitelli, T., Chiusano, S., Garza, P., Xiao, X. (2016) Predicting critical conditions in bicycle sharing systems. Computing. doi:10.1007/s00607-016-0505-x
  9. Camporelli, M.: Using a Bayesian Classifier for Probability Estimation: Analysis of the AMIS Score for Risk Stratification in Myocardial Infarction.  Diploma Thesis, Department of Informatics, University of Zurich (2006).
  10. Corani, G., Magli, C., Giusti, A., Gianaroli, L., & Gambardella, L. M. (2013). A Bayesian network model for predicting pregnancy after in vitro fertilization. Computers in Biology and Medicine, 43(11), 1783-1792.
  11. Correa, S., Cerqueira, R.: Statistical Approaches to Predicting and Diagnosing Performance Problems in Component-Based Distributed Systems: An Experimental Evaluation. In Proceedings 4th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), (2010) 21-30.
  12. Cruz, L., Perez, J., Landero, V., del Angel, E. S., Alvarez, V. M., Perez, V. (2004) An Ordered Preprocessing Scheme for Data Mining. Proceedings of PRICAI 2004: Trends in Artificial Intelligence: 8th Pacific Rim International Conference on Artificial Intelligence. Berlin: Springer, pp. 1007 . 1008.
  13. De Ferrari, L. (2005) Mining housekeeping genes with a naive Bayes classifier. MSc Thesis, University of Edinburgh, School of Informatics.
  14. De Ferrari, L., Aitken, S.: Mining housekeeping genes with a naive Bayes classifier. BMC Genomics 7(1) (2006) 277.
  15. Faulhaber, A. (2005) Enhancing hypernym extraction for named entities using machine learning based classification. Bachelor's thesis, Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Heidelberg.
  16. Flikka, K., Martens, L., Vandekerckhove, J., Gevaert, K., and Eidhammeri, I. (2005) Improving throughput and reliability of peptide identifications through spectrum quality evaluation. In Proceedings of the 9th Annual International Conference on Research in Computational Molecular Biology.
  17. Flikka, K., Martens, L., Vandekerckhove, J., Gevaert, K., Eidhammer, I.: Improving the reliability and throughput of mass spectrometry-based proteomics by spectrum quality filtering. Proteomics 6(7) (2006) 2086–2094.
  18. Garcia, B., Aler, R., Ledezma, A., Sanchis, A.: Protein-protein functional association prediction using genetic programming. In: Proceedings of the Tenth Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, ACM. (2008) 347–348.
  19. García-Jiménez B, Juan D, Ezkurdia I, Andrés-León E, Valencia A.: Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach. PLoS ONE (2010) 5(4): e9969. doi:10.1371/journal.pone.0009969
  20. Hopfgartner, F., Urruty, T., Lopez, P.B., Villa, R., Jose, J.M: Simulated evaluation of faceted browsing based on feature selection. Multimedia Tools and Applications 47(3) (2010) 631-662.
  21. Htike, Z. Z., & Win, S. L.. (2013) Classification of Eukaryotic Splice-junction Genetic Sequences Using Averaged One-dependence Estimators with Subsumption Resolution. In Procedia Computer Science. Vol. 23, pp. 36-43.
  22. Hunt, K.: Evaluation of Novel Algorithms to Optimize Risk Stratification Scores in Myocardial Infarction. PhD thesis, Department of Informatics, University of Zurich (2006).
  23. Kluwer, Tina, Uszkoreit, Hans and Xu, Feiyu: Using syntactic and semantic based relations for dialogue act recognition. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (2010) 570-578.
  24. Kofod, C., & Ortiz-Arroyo, D. (2008). Exploring the design space of symbolic music genre classification using data mining techniques. In Computational Intelligence for Modelling Control & Automation, 2008 International Conference on (pp. 43-48). IEEE.
  25. Kovacs, G., Hajdu, A.: Extraction of vascular system in retina images using Averaged One-Dependence Estimators and orientation estimation in Hidden Markov Random Fields. In: Proc. 2011 IEEE Int. Symp. Biomedical Imaging (2011) 693.696.
  26. Kunchevaa, L.I., Vilas, V.J.D.R., Rodriguezc, J.J.: Diagnosing scrapie in sheep: A classification experiment. Computers in Biology and Medicine 37(8) (2007) 1194–1202.
  27. Kurz, D., Bernstein, A., Hunt, K., Radovanovic, D., Erne, P., Siudak, Z., Bertel, O.: Simple point-of-care risk stratification in acute coronary syndromes: the AMIS model. Heart 95(8) (2009) 662-668.
  28. Lasko, T. A. (2004) When my patient is not my patient: inferring primary-care relationships using machine learning. Thesis. Master of Health Sciences and Technology. MIT.
  29. Lasko, T.A., Atlas, S.J., Barry, M.J., Chueh, K.H.C.: Automated identification of a physician’s primary patients. Journal of the American Medical Informatics Association 13(1) (2006) 74–79.
  30. Lau, Q.P., Hsu, W., Lee, M.L., Mao, Y., Chen, L.: Prediction of cerebral aneurysm rupture. In: Proceedings of the nineteenth IEEE International Conference on Tools with Artificial Intelligence, Washington, DC, USA, IEEE Computer Society (2007) 350–357.
  31. Leon, E.A., Ezkurdia, I., Garcia, B., Valencia, A., Juan, D.: EcID. A database for the inference of functional interactions in E. coli. Nucleic Acids Research 37(Database issue) (2009) D629.
  32. Liew, CY., Ma, XH., Yap, CW.: Consensus model for identification of novel PI3K inhibitors in large chemical library. Journal of Computer-Aided Molecular Design. 24(2) (2010) 131-141.
  33. Marincic, D., Tusar, T., Gams, M., Sef, T.: Analysis of Automatic Stress Assignment in Slovene. Informatica 20(1) (2009) 35-50.
  34. Masegosa, AR.,  Joho, H., Jose JM.: Evaluating Query-Independent Object Features for Relevancy Prediction. In Advances in Information Retrieval. Springer Berlin. (2007) 283-294.
  35. Murakami, Y., Kenji M. Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators. BMC Bioinformatics 15(1) (2014): 213.
  36. Najadat, H, Alsmadi, I.: Enhance Rule Based Detection for Software Fault Prone Modules. International Journal of Software Engineering and Its Applications 6(1) (2012) 75-84.
  37. Navarretta, C: Predicting speech overlaps from speech tokens and co-occurring body behaviours in dyadic conversations. In Proceedings of the 15th ACM on International conference on multimodal interaction, ACM (2013) 157-164.
  38. Nikora, A.P.: Classifying requirements: Towards a more rigorous analysis of natural-language specifications. In: Proceedings of the Sixteenth IEEE International Symposium on Software Reliability Engineering, Washington, DC, USA, IEEE Computer Society (2005) 291–300.
  39. Orhan, Z., Altan, Z.: Impact of feature selection for corpus-based WSD in Turkish. In: Proceedings of the fifth Mexican International Conference on Artificial Intelligence, Springer Berlin / Heidelberg (2006) 868–878.
  40. Shahri, SH., Jamil, H.: An Extendable Meta-learning Algorithm for Ontology Mapping. In Flexible Query Answering Systems, Springer Berlin (2009) 418-430.
  41. Simpson, M., Demner-Fushman, D., Sneiderman, C., Antani, S., Thoma, G.: Using non-lexical features to identify effective indexing terms for biomedical illustrations. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics (2009) 737–744.
  42. Speckauskiene, V., Lukosevicius, A.: Methodology of Adaptation of Data Mining Methods for Medical Decision Support: Case Study. Electronics and Electrical Engineering 90 (2009) 25-28.
  43. Tenório, J.; Hummel, A.; Cohrs, F.; Sdepanian, V.; Pisa, I. & de Fátima Marin, H. Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease. International Journal of Medical Informatics, Elsevier, 2011, pp. 793-802.
  44. Tian, Y., Chen, C., Zhang, C: AODE for Source Code Metrics for Improved Software Maintainability. Fourth International Conference on Semantics, Knowledge and Grid (2008) pp.330-335.
  45. Wang, H., Klinginsmith, J., Dong, X., Lee, A., Guha, R., Wu, Y., Crippen, G., Wild, D.: Chemical data mining of the NCI human tumor cell line database. Journal of Chemical Information and Modeling 47(6) (2007) 2063–2076.
  46. Win, S.L.; Htike, Z. Z.; Yusof, F.; Noorbatcha, I. A. Cancer Recognition From DNA Microarray Gene Expression Data Using Averaged One-Dependence Estimators. International Journal on Cybernetics & Informatics 3(1) (2014).
  47. Yang, Y.; Li, Z.; Nan, P. & Zhang, X. Drug-induced glucose-6-phosphate dehydrogenase deficiency-related hemolysis risk assessment. Computational Biology and Chemistry, 2011, 35, 189 - 192