Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison tomost techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithmsmake use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate ∗Corresponding author. Tel.: +55 16 8138 3627. Email addresses: [email protected] (Rodrigo C. Barros), [email protected] (Duncan D. Ruiz), [email protected] (Mrcio P. Basgalupp) Preprint submitted to Information Sciences January 15, 2013 heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach E-Motion (Evolutionary Model Tree Induction). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications.