Developments on a Multi-objective Metaheuristic (MOMH) Algorithm for Finding Interesting Sets of Classification Rules

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In this paper, we experiment with a combination of innovative approaches to rule induction to encourage the production of interesting sets of classification rules. These include multi-objective metaheuristics to induce the rules; measures of rule dissimilarity to encourage the production of dissimilar rules; and rule clustering algorithms to evaluate the results obtained. Our previous implementation of NSGA-II for rule induction produces a set of cc-optimal rules (coverage-confidence optimal rules). Among the set of rules produced there may be rules that are very similar. We explore the concept of rule similarity and experiment with a number of modifications of the crowding distance to increasing the diversity of the partial classification rules produced by the multi-objective algorithm.
Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization
EditorsCarlos Coello Coello, Arturo Hernández Aguirre, Eckart Zitzler
PublisherSpringer Berlin / Heidelberg
Number of pages15
ISBN (Print)978-3-540-24983-2
Publication statusPublished - 2005

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg

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