Evolutionary Learning of Hierarchical Decision Rules

Abstract

This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must be, therefore, tried in order until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We have tested our system on real data from the UCI Repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice.