Genetic rule induction at an intermediate level

Lists of if±then rules (i.e. ordered rule sets) are among the most expressive and intelligible representations for inductive learning algorithms. Two extreme strategies searching for such a list of rules can be distinguished: (i) local strategies primarily based on a step-by-step search for the optimal list of rules, and (ii) global strategies primarily based on a one-strike search for the optimal list of rules. Both approaches have their disadvantages. In this paper we present an intermediate strategy. A sequential covering strategy is combined with a one-strike genetic search for the next most promising rule. To achieve this, a new rule-®tness function is introduced. Experimental results on benchmark problems are presented and the performance of our intermediate approach is compared with other rule learning algorithms. Finally, GeSeCo's performance is compared to a more local strategy on a set of tasks in which the information value of individual attributes is varied.