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Discovering rules with a genetic sequential covering algorithm (GeSeCo)

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 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 most promising next rule. To achieve this, a new rule-tness function is introduced. Experimental results are reported in which the performance of our intermediate approach is compared to other rule learning algorithms.