Abstract
Classical decision trees proved to be very good induction systems providing accurate prediction and rule based representation. However, in some areas the application of the classical decision trees is limited and more advanced and more complex trees have to be used. One of the examples of such trees are distance based trees, where a node function (test) is defined by a prototype, distance measure and threshold. Such trees can be easily obtained from classical decision trees by initial data preprocessing. However, this solution dramatically increases computational complexity of the tree. This paper presents a clustering based approach to computational complexity reduction. It also discusses aspects of interpretation of the obtained prototype-threshold rules.
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References
Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting and variants. ML 36, 105–142 (1999)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Breiman, L., Friedman, J.H., Olshen, A., Stone, C.J.: Classification and regression trees. Wadsworth, Belmont (1984)
Corchado, E., Abraham, A., de Carvalho, C.A.: Hybrid intelligent algorithms and applications. Information Sciences 180(14), 2633–2634 (2010)
Corchado, E., Grana, M., Woźniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)
Duch, W., Grudziński, K.: Prototype based rules - new way to understand the data. In: IEEE International Joint Conference on Neural Networks, pp. 1858–1863. IEEE Press, Washington, D.C (2001)
Grąbczewski, K., Duch, W.: Heterogeneous Forests of Decision Trees. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 504–509. Springer, Heidelberg (2002)
Kohavi, R.: Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In: Second International Conference on Knowledge Discovery and Data Mining, pp. 202–207 (1996)
Kordos, M., Blachnik, M., Wieczorek, T., Golak, S.: Neural Network Committees Optimized with Evolutionary Methods for Steel Temperature Control. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 42–51. Springer, Heidelberg (2011)
Kordos, M., Blachnik, M., Wieczorek, T.: Temperature prediction in electric arc furnace with neural network tree. In: Honkela, T., et al. (eds.) ICANN 2011, Part II. LNCS, vol. 6792, pp. 71–78. Springer, Heidelberg (2011)
Kordos, M., Blachnik, M., Perzyk, M., Kozłowski, J., Bystrzycki, O., Gródek, M., Byrdziak, A., Motyka, Z.: A Hybrid System with Regression Trees in Steel-Making Process. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part I. LNCS, vol. 6678, pp. 222–230. Springer, Heidelberg (2011)
Blachnik, M., Duch, W., Wieczorek, T.: Selection of Prototype Rules: Context Searching Via Clustering. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 573–582. Springer, Heidelberg (2006)
Landwehr, N., Mark Hall, E.F.: Logistic model trees. ML 95(1-2), 161–205 (2005)
Quinlan, J.R.: C 4.5: Programs for machine learning. Morgan Kaufmann, San Mateo (1993)
Rosch, R.H.: Cognitive reference points. Cognitive Psychology 4(7) (1975)
Roth, I., Bruce, V.: Perception and Representation, 2nd edn. Open University Press (1995)
Stanfill, C., Waltz, D.: Toward memory-based reasoning. Communications of the ACM 29(12), 1213–1228 (1986)
Duch, W., Blachnik, M.: Fuzzy Rule-Based Systems Derived from Similarity to Prototypes. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 912–917. Springer, Heidelberg (2004)
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Blachnik, M., Kordos, M. (2012). Computational Complexity Reduction and Interpretability Improvement of Distance-Based Decision Trees. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_26
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DOI: https://doi.org/10.1007/978-3-642-28942-2_26
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