Discrete support vector decision trees via tabu search

Abstract

An algorithm is proposed for generating decision trees in which multivariate splitting rules are based on the new concept of discrete support vector machines. By this term a discrete version of SVMs is denoted in which the error is properly expressed as the count of misclassified instances, in place of a proxy of the misclassification distance considered by traditional SVMs. The resulting mixed integer programming problem formulated at each node of the decision tree is then efficiently solved by a tabu search heuristic. Computational tests performed on both well-known benchmark and large marketing datasets indicate that the proposed algorithm consistently outperforms other classification approaches in terms of accuracy, and is therefore capable of good generalization on validation sets.