In this paper we propose a new evolutionary algorithm for global induction of oblique model trees that associates leaves with multiple linear regression models. In contrast to the typical top-down approaches it globally searches for the best tree structure, splitting hyper-planes in internal nodes and models in the leaves. The general structure of proposed solution follows a typical framework of evolutionary algorithms with an unstructured population and a generational selection. We propose specialized genetic operators to mutate and cross-over individuals (trees). The fitness function is based on the Bayesian Information Criterion. In preliminary experimental evaluation we show the impact of the tree representation on solving different prediction problems. © 2013 Springer-Verlag.
CITATION STYLE
Czajkowski, M., & Kretowski, M. (2013). Global induction of oblique model trees: An evolutionary approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7895 LNAI, pp. 1–11). https://doi.org/10.1007/978-3-642-38610-7_1
Mendeley helps you to discover research relevant for your work.