Learning Decision Rules by Means of Hybrid-Encoded Evolutionary Algorithms

Abstract

This paper describes an approach based on evolutionary algorithms, HIDER (HIerarchical DEcision Rules), 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. In addition, the algorithm tries to obtain more understandable rules by minimizing the number of attributes involved. The evolutionary algorithm uses binary coding for discrete attributes and integer coding for continuous attributes. The integer coding consists in defining indexes to the values that have greater probability of being used as boundaries in the conditions of the rules. Thus, the individuals handles these indexes instead of the real values. We have tested our system on real data from the UCI Repository, and the results of a 10-fold cross-validation are compared to C4.5s and C4.5Rules. The experiments show that HIDER works well in practice.