Fuzzy genetic algorithm approach to feature selection problem

George S.K. Fung, James N.K. Liu, K. H. Chan, Rynson W.H. Lau

Research output: Contribution to conferencePaperpeer-review

Abstract

The primary role of genetic algorithm is in the selective breeding of a population of individuals. Suboptimal solution can be obtained by such an algorithm which applies the paradigm of various evolutionary selection and searches through many generations via different genetic operators. The selection of features associated with each individual is based on the fitness-proportionate selection in which parents are chosen from the population. This fitness is a problem-specific property that describes an individual's performance upon some chosen features quantitatively. This paper describes a formal fuzzy genetic algorithm to overcome the traditional problems in feature classification and selection and provides fuzzy templates for the identification of the smallest subset of features. Simulation results demonstrate that the operation using soft crossover significantly improves the searching power through the multi-dimensional feature space. Further improvement on the application of the techniques is undergoing.

Original languageEnglish (US)
Pages441-446
Number of pages6
StatePublished - Jan 1 1997
EventProceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3) - Barcelona, Spain
Duration: Jul 1 1997Jul 5 1997

Other

OtherProceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3)
CityBarcelona, Spain
Period7/1/977/5/97

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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