Abstract
An analysis of the Separability of Split Value criterion in some particular applications has led to conclusions about possible improvements of the criterion. Here, the new formulation of the SSV criterion is presented and examined. The results obtained for 21 different benchmark datasets are presented and discussed in comparison with the most popular decision tree node splitting criteria like information gain and Gini index. Because the new SSV definition introduces a parameter, some empirical analysis of the new parameter is presented. The new criterion turned out to be very successful in decision tree induction processes.
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References
Breiman, L., Friedman, J.H., Olshen, A., Stone, C.J.: Classification and regression trees. Wadsworth, Belmont (1984)
Buntine, W., Niblett, T.: A further comparison of splitting rules for decision-tree induction. Machine Learning 8, 75–85 (1992), http://dx.doi.org/10.1007/BF00994006 , doi:10.1007/BF00994006
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Grąbczewski, K., Duch, W.: A general purpose separability criterion for classification systems. In: Proceedings of the 4th Conference on Neural Networks and Their Applications, Zakopane, Poland, pp. 203–208 (June 1999)
Grąbczewski, K., Duch, W.: The Separability of Split Value criterion. In: Proceedings of the 5th Conference on Neural Networks and Their Applications, Zakopane, Poland, pp. 201–208 (June 2000)
Grąbczewski, K., Jankowski, N.: Versatile and efficient meta-learning architecture: Knowledge representation and management in computational intelligence. In: IEEE Symposium Series on Computational Intelligence (SSCI 2007), pp. 51–58. IEEE, Los Alamitos (2007)
Grąbczewski, K., Jankowski, N.: Efficient and friendly environment for computational intelligence. In: Knowledge-Based Systems, p. 41 (2011) (in print)
Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific, Singapore (2008)
Mingers, J.: An empirical comparison of selection measures for decision-tree induction. Machine Learning 3, 319–342 (1989)
Quinlan, J.R.: Programs for machine learning (1993)
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Grąbczewski, K. (2011). Separability of Split Value Criterion with Weighted Separation Gains. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_7
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DOI: https://doi.org/10.1007/978-3-642-23199-5_7
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