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Using Evolutionary Algorithms to Induce Oblique Decision Trees

This paper illustrates the application of evolutionary algorithms (EAs) to the problem of oblique decision tree induction. The objectives are to demonstrate that EAs can find classifiers whose accuracy is competitive with other oblique tree construction methods, and that at least in some cases this can be accomplished in a shorter time. Experiments were performed with a (1+1) evolution strategy and a simple genetic algorithm on public domain and artificial data sets. The empirical results suggest that the EAs quickly find competitive classifiers, and that EAs scale up better than traditional methods to the dimensionality of the domain and the number of instances used in training.