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

  • Beatriz de la Iglesia
  • Alan Reynolds
  • Vic J Rayward-Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3410)

Abstract

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.

Keywords

Association Rule Pareto Front Categorical Attribute Pareto Optimal Front Rule Induction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Beatriz de la Iglesia
    • 1
  • Alan Reynolds
    • 1
  • Vic J Rayward-Smith
    • 1
  1. 1.University of East AngliaNorwich, NorfolkUK

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