Rule Induction Using Multi-Objective Metaheuristics: Encouraging Rule Diversity
Alan Reynolds, Beatriz de la Iglesia · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Previous research produced a multi-objective metaheuristic for partial classification, where rule dominance is determined through the comparison of rules based on just two objectives: rule confidence and coverage. The user is presented with a set of descriptions of the class of interest from which he may select a subset. This paper presents two enhancements to this algorithm, describing how the use of modified dominance relations may increase the diversity of rules presented to the user and how clustering techniques may be used to aid in the presentation of the potentially large sets of rules generated.