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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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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