Abstract
In the paper, a new method of decision tree learning for cost-sensitive classification is presented. In contrast to the traditional greedy top-down inducer in the proposed approach optimal trees are searched in a global manner by using an evolutionary algorithm (EA). Specialized genetic operators are applied to modify both the tree structure and tests in non-terminal nodes. A suitably defined fitness function enables the algorithm to minimize the misclassification cost instead of the number of classification errors. The performance of the EA-based method is compared to three well-recognized algorithms on real-life problems with known and randomly generated cost-matrices. Obtained results show that the proposed approach is competitive both in terms of misclassification cost and compactness of the classifier at least for some datasets.
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References
Abe, N., Zadrozny, B., Langford, J.: An iterative method for multi-class cost-sensitive learning. In: KDD’04, pp. 3–11. ACM Press, New York (2004)
Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases, (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Bradford, J.P., Kunz, C., Kohavi, R., Brunk, C., Brodley, C.E.: Pruning decision trees with misclassification costs. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 131–136. Springer, Heidelberg (1998)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth Int. Group, Belmont (1984)
Cantu-Paz, E., Kamath, C.: Inducing oblique decision trees with evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7(1), 54–68 (2003)
Chai, B., et al.: Piecewise-linear classifiers using binary tree structure and genetic algorithm. Pattern Recognition 29(11), 1905–1917 (1996)
Domingos, P.: MetaCost: A general method for making classifiers cost-sensitive. In: Proc. of KDD’99, pp. 155–164. ACM Press, New York (1999)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proc. of IJCAI’93, pp. 1022–1027 (1993)
Frank, E., et al.: Weka 3 - Data Mining with Open Source Machine Learning Software in Java. University of Waikato (2000), http://www.cs.waikato.ac.nz/~ml/weka
Knoll, U., Nakhaeizadeh, G., Tausend, B.: Cost-sensitive pruning of decision trees. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 383–386. Springer, Heidelberg (1994)
Koza, J.: Concept formation and decision tree induction using genetic programming paradigm. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 124–128. Springer, Heidelberg (1991)
Krȩtowski, M.: An evolutionary algorithm for oblique decision tree induction. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 432–437. Springer, Heidelberg (2004)
Krȩtowski, M., Grześ, M.: Global learning of decision trees by an evolutionary algorithm. In: Information Processing and Security Systems, pp. 401–410. Springer, Heidelberg (2005)
Krȩtowski, M., Grześ, M.: Evolutionary learning of linear trees with embedded feature selection. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 400–409. Springer, Heidelberg (2006)
Krȩtowski, M., Grześ, M.: Mixed decision trees: An evolutionary approach. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 260–269. Springer, Heidelberg (2006)
Kwedlo, W., Krȩtowski, M.: An evolutionary algorithm for cost-sensitive decision rule learning. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 288–299. Springer, Heidelberg (2001)
Ling, C., Yang, Q., Wang, J., Zhang, S.: Decision trees with minimal costs. In: Proc. of ICML’04, Article No. 69, ACM Press, New York (2004)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Margineantu, D.D., Dietterich, T.G.: Bootstrap methods for the cost-sensitive evaluation of classifiers. In: Proc. of ICML’2000, pp. 583–590. Morgan Kaufmann, San Francisco (2000)
Papagelis, A., Kalles, D.: Breeding decision trees using evolutionary techniques. In: Proc. of ICML’01, pp. 393–400. Morgan Kaufmann, San Francisco (2001)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Ting, K.M.: An instance-weighting method to induce cost-sensitive trees. IEEE Transactions on Knowledge and Data Engineering 14(3), 659–665 (2002)
Turney, P.: Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2, 369–409 (1995)
Turney, P.: Types of cost in inductive concept learning. In: Proc. of ICML’2000 Workshop on Cost-Sensitive Learning, Stanford, CA (2000)
Zadrozny, B., Langford, J., Abe, N.: Cost-sensitive learning by cost-proportionate example weighting concept learning. In: Proc. of ICDM’03, IEEE Computer Society Press, Los Alamitos (2003)
Zhang, S., Qin, Z., Ling, C., Sheng, S.: Missing is usefull: Missing values in cost-sensitive decision trees. IEEE Transactions on Knowledge and Data Engineering 17(12), 1689–1693 (2005)
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Krȩtowski, M., Grześ, M. (2007). Evolutionary Induction of Decision Trees for Misclassification Cost Minimization. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_1
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DOI: https://doi.org/10.1007/978-3-540-71618-1_1
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