Abstract
In many cases it is better to extract a set of decision trees and a set of possible logical data descriptions instead of a single model. The trees that include premises with constraints on the distances from some reference points are more flexible because they provide nonlinear decision borders. Methods for creating heterogeneous forests of decision trees based on Separability of Split Value (SSV) criterion are presented. The results confirm their usefulness in understanding data structures.
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Duch W, Adamczak R. and Grcabczewski K. (2001) Methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Transactions on Neural Networks 12: 277–306
Duch W, Grcabczewski K. (2002) Heterogeneous adaptive systems. World Congress of Computational Intelligence, Honolulu, May 2002
Grcabczewski K, Duch W. (1999) A general purpose separability criterion for classification systems, 4th Conference on Neural Networks and Their Applications, Zakopane, Poland, pp. 203–208
Grcabczewski K. Duch W. (2000) The Separability of Split Value Criterion, 5th Conference on Neural Networks and Soft Computing, Zakopane, Poland, pp. 201–208
Blake, C.L, Merz, C.J. (1998) UCI Repository of machine learning databases http://www.ics.uci.edu/ mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science.
Bobrowski L, Kretowska M, Kretowski M. (1997) Design of neural classifying networks by using dipolar criterions. 3rd Conf. on Neural Networks and Their Applications, Kule, Poland
Breiman L. (1998) Bias-Variance, regularization, instability and stabilization. In: Bishop, C. (Ed.) Neural Networks and Machine Learning. Springer, Berlin, Heidelberg, New York
Dietterich T. (1998) Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms, Neural Computation 10, 1895–1923
S.M. Weiss, I. Kapouleas. “An empirical comparison of pattern recognition, neural nets and machine learning classification methods”, in: Readings in Machine Learning, eds. J.W. Shavlik, T.G. Dietterich, Morgan Kauffman Publ, C A 1990
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Grąbczewski, K., Duch, W. (2002). Heterogeneous Forests of Decision Trees. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_82
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DOI: https://doi.org/10.1007/3-540-46084-5_82
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