Automated classification tree evolution through hybrid metaheuristics

Abstract

In present, data processing is an important process in many organizations. Classification trees are used to assign a classification to unknown data and can be also used for data partitioning (data clustering). The classification tree must be able to cope with outliers and have acceptably simple structure. An important advantage is the white-box structure. This paper presents a novel method called ACO-DTree for classification tree generation and their evolution inspired by natural processes. It uses a hybrid metaheuristics combining evolutionary strategies and ant colony optimization. Proposed method benefits from the stochastic process and population approach, which allows the algorithm to evolve more efficiently than the methods alone. The paper also consults the parameter estimation for the method. Tests on real data (UCI and MIT-BIH database) have been performed and evaluated. © 2007 Springer-Verlag Berlin Heidelberg.