In the paper a new evolutionary algorithm for induction of univariate regression trees is proposed. In contrast to typical top-down approaches it globally searches for the best tree structure and tests in internal nodes. The population of initial trees is created with diverse top-down methods on randomly chosen sub-samples of the training data. Specialized genetic operators allow the algorithm to efficiently evolve regression trees. The complexity term introduced in the fitness function helps to mitigate the over-fitting problem. The preliminary experimental validation is promising as the resulting trees can be significantly less complex with at least comparable performance to the classical top-down counterpart. © 2010 Springer-Verlag.
CITATION STYLE
Krȩtowski, M., & Czajkowski, M. (2010). An evolutionary algorithm for global induction of regression trees. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6114 LNAI, pp. 157–164). https://doi.org/10.1007/978-3-642-13232-2_19
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