An entropy-based adaptive genetic algorithm for learning classification rules

L. Yang, D. H. Widyantoro, T. Ioerger, J. Yen

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

25 Scopus citations

Abstract

Genetic algorithm is one of the commonly used approaches on data mining. In this paper, we put forward a genetic algorithm approach for classification problems. Binary coding is adopted in which an individual in a population consists of a fixed number of rules that stand for a solution candidate. The evaluation function considers four important factors which are error rate, entropy measure, rule consistency and hole ratio, respectively. Adaptive asymmetric mutation is applied by the self-adaptation of mutation inversion probability from 1-0 (0-1). The generated rules are not disjoint but can overlap. The final conclusion for prediction is based on the voting of rules and the classifier gives all rules equal weight for their votes. Based on three databases, we compared our approach with several other traditional data mining techniques including decision trees, neural networks and naive bayes learning. The results show that our approach outperformed others on both the prediction accuracy and the standard d eviation.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation, ICEC
Pages790-796
Number of pages7
Volume2
StatePublished - 2001
EventCongress on Evolutionary Computation 2001 - Seoul, Korea, Republic of
Duration: May 27 2001May 30 2001

Other

OtherCongress on Evolutionary Computation 2001
CountryKorea, Republic of
CitySeoul
Period5/27/015/30/01

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Cite this

Yang, L., Widyantoro, D. H., Ioerger, T., & Yen, J. (2001). An entropy-based adaptive genetic algorithm for learning classification rules. In Proceedings of the IEEE Conference on Evolutionary Computation, ICEC (Vol. 2, pp. 790-796)