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Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2), 139–157. Dobkin, D., Gunopulos, D., & Kasif, S. (1996). Computing optimal shallow decision trees. In Proceedings of the 4th International Symposium on Artificial Intelligence and Mathematics (ISAIM-1996). Domingos, P. (1999a). The role of Occam’s razor in knowledge discovery. Data Mining and Knowledge Discovery, 3(4), 409–425. Domingos, P. (1999b). Metacost: A general method for making classifiers costsensitive. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining (KDD’1999), pp. 155–164. Dong, M., & Kothari, R. (2001). Look-ahead based fuzzy decision tree induction. IEEE-FS, 9, 461–468. Dougherty, J., Kohavi, R., & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In Proceedings of the 12th International Conference on Machine Learning (ICML-1995), pp. 194–202, Tahoe City, California, USA. Drummond, C., & Holte, R. (2000). Exploiting the cost (in)sensitivity of decision tree splitting criteria. In In Proceedings of the 17th International Conference on Machine Learning (ICML-2000), pp. 239–246. Morgan Kaufmann. Elkan, C. (2001). The foundations of cost-sensitive learning. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI- 2001), pp. 973–978, Seattle, Washington, USA. Elomaa, T. (1994). In defense of c4.5: Notes on learning one-level decision trees. In In Proceedings of the 11th International Conference on Machine Learning (ICML-1994), pp. 62–69. Morgan Kaufmann. Esmeir, S., & Markovitch, S. (2004). Lookahead-based algorithms for anytime induction of decision trees. In Proceedings of the 21st International Conference on Machine Learning (ICML-2004). Esmeir, S., & Markovitch, S. (2006). When a decision tree learner has plenty of time. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-2006), Boston, MA, USA. Esmeir, S., & Markovitch, S. (2007a). Anytime induction of cost-sensitive trees. In Proceedings of The 21st Annual Conference on Neural Information Processing Systems (NIPS-2007), Vancouver, B.C., Canada. Esmeir, S., & Markovitch, S. (2007b). Anytime learning of decision trees. Journal of Machine Learning Research, 8. 158
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