An Evolutionary Algorithm for Global Induction of Regression Trees

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 topdown 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.

• Type: Conference
• Year: 2010
• Where: ICAISC
• Authors: Marek Kretowski, Marcin Czajkowski
• Keywords: Artificial Intelligence, ICAISC 2010, Regression Trees, Typical Top-down Approaches, Univariate Regression Trees