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Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10

DOI: 10.1145/1774088.1774327

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Evolutionary model tree induction

Journal article published in 2010 by Rodrigo C. Barros ORCID, Marcio P. Basgalupp, Duncan D. Ruiz, Andre Carlos Ponce Leon Ferreira de Carvalho, Alex Alves Freitas
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This paper is available in a repository.

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Abstract

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output with an acceptable level of predictive performance. Since generating optimal model trees is a NP-Complete problem, the traditional model tree induction algorithms make use of a greedy heuristic, which may not converge to the global optimal solution. We propose the use of the evolutionary algorithms paradigm (EA) as an alternate heuristic to generate model trees in order to improve the convergence to global optimal solutions. We test the predictive performance of this new approach using public UCI datasets, and compare the results with traditional greedy regression/model trees induction algorithms.