Discovering hierarchical decision rules with evolutive algorithms in supervised learning

Abstract

This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in continuous and discrete domains based on evolutive algorithms. The algorithm produces a hierarchical set of rules, that is, the rules must be applied in a speciÞc order. With this policy, the number of rules may be reduced because the rules could be one inside of another. The evolutive algorithm uses both real and binary codiÞcation for the individuals of the population and introduces several new genetic operators. In addition, this paper discusses the capability of learning systems based on an evolutive algorithm to reduce both the number of rules and the number of attributes involved in the rule set. We have tested our system on real data from the UCI repository. The results of a 10-fold cross validation are compared to C4.5 s and they show an important improvement.