In the paper, a new evolutionary algorithm (EA) for mixed tree learning is proposed. In non-terminal nodes of a mixed decision tree different types of tests can be placed, ranging from a typical univariate inequality test up to a multivariate test based on a splitting hyperplane. In contrast to classical top-down methods, our system searches for an optimal tree in a global manner, i.e. it learns a tree structure and tests in one run of the EA. Specialized genetic operators allow for generating new sub-trees, pruning existing ones as well as changing the node type and the tests. The proposed approach was experimentally verified on both artificial and real-life data and preliminary results are promising. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Krȩtowski, M., & Grześ, M. (2006). Mixed decision trees: An evolutionary approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4081 LNCS, pp. 260–269). Springer Verlag. https://doi.org/10.1007/11823728_25
Mendeley helps you to discover research relevant for your work.