Pareto-optimality of oblique decision trees from evolutionary algorithms

Abstract

This paper investigates the performance of evolutionary algorithms in the optimization aspects of oblique decision tree construction and describes their performance with respect to classification accuracy, tree size, and Pareto-optimality of their solution sets. The performance of the evolutionary algorithms is analyzed and compared to the performance of exhaustive (traditional) decision tree classifiers on several benchmark datasets. The results show that the classification accuracy and tree sizes generated by the evolutionary algorithms are comparable with the results generated by traditional methods in all the sample datasets and in the large datasets, the multiobjective evolutionary algorithms generate better Pareto-optimal sets than the sets generated by the exhaustive methods. The results also show that a classifier, whether exhaustive or evolutionary, that generates the most accurate trees does not necessarily generate the shortest trees or the best Pareto-optimal sets.