Abstract
Decision trees generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized into several intervals using a set of crisp cut points. One drawback of decision trees is their instability, i.e., small data deviations may require a significant reconstruction of the decision tree. Here, we present a novel soft decision tree method that uses soft of fuzzy discretization instead of traditional crisp cuts. We use a resampling based technique to generate soft discretization points and demonstrate the advantages of using our resampling based soft discretization over traditional crisp methods.
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References
Quinlan, J.R.: Decision Trees and Decision Making. IEEE Transactions on System, Man and Cybernetic, 339–346 (1990)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International, San Francisco (1984)
Quinlan, J.R.: C4.5: Programs for Machine Learning. M. Kaufmann, SanMateo (1993)
Kerber, R.: Discretization of Numeric Attributes. In: Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 123–128. MIT Press, Cambridge (1990)
Fayyad, U.M., Irani, K.: Multi-interval Discretization of Continuous-Valued Attributes for Classification Learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, San Mateo (1993)
Efron, B., Tibshirani, R.: An Introduction to the Bootstrap. Chapman and Hall, Boca Raton (1998)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Zadeh, L.A.: Fuzzy Sets as a bases for a Theory of Possibility. Fuzzy Sets and Systems, 3–28 (1978)
Olaru, C., Wehenkel, L.: A complete fuzzy decision tree technique. Fuzzy Sets and Systems 138, 221–254 (2003)
Ramdani, M.: System d’Induction Formelle a base de Connaissanses Impresises. These de l’universite P et M Curie, rapport 94/1, LAFORIA IBP (1994)
Wang, T., Zhoujun, L., Yuejin, Y., Huowang, C.: A Survey of Fuzzy Decision Tree Classifier Methodology. In: ICFIE, pp. 959–968 (2007)
Shannon, C.E., Weaver, W.: The mathematical Theory of Communication. University of Illinois Press, Urbana (1949)
Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(1), 1–14 (1998)
Ichihashi, H., Shirai, T., Nagasaka, K., Miyoshi, T.: Neuro fuzzy ID3: A method of inducing fuzzy decision trees with linear programming for maximizing entropy and algebraic methods. Fuzzy Sets System 81(1), 157–167 (1996)
Xizhao, W., Hong, J.: On the handling of fuzziness for continuous valued attributes in decision tree generation. Fuzzy Sets System 99, 283–290 (1998)
Murthy, S.K., Kasif, S., Salzberg, S.: A System for Induction of Oblique Decision Trees. Journal of AI Research (1994)
Marsala, C.: Application of Fuzzy Rule Induction to Data Mining. In: Andreasen, T., Christiansen, H., Larsen, H.L. (eds.) FQAS 1998. LNCS (LNAI), vol. 1495, pp. 260–271. Springer, Heidelberg (1998)
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Qureshi, T., Zighed, D.A. (2009). A Soft Discretization Technique for Fuzzy Decision Trees Using Resampling. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_71
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DOI: https://doi.org/10.1007/978-3-642-04394-9_71
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