A two-phase evolutionary algorithm for multiobjective mining of classification rules

Yung Hsiang Chan*, Tsung Che Chiang, Li Chen Fu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

Classification rule mining, addressed a lot in machine learning and statistics communities, is an important task to extract knowledge from data. Most existing approaches do not particularly deal with data instances matched by more than one rule, which results in restricted performance. We present a two-phase multiobjective evolutionary algorithm which first aims at searching decent rules and then takes the rule interaction into account to produce the final rule sets. The algorithm incorporates the concept of Pareto dominance to deal with trade-off relations in both phases. Through computational experiments, the proposed algorithm shows competitive to the state-of-the-art. We also study the effect of a niching mechanism.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain
Duration: 2010 Jul 182010 Jul 23

Publication series

Name2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Country/TerritorySpain
CityBarcelona
Period2010/07/182010/07/23

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Applied Mathematics

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