Towards an evolutionary algorithm: A comparison of two feature selection algorithms

Kan Chen, Huan Liu

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

14 Scopus citations

Abstract

In order to deal with a large number of attributes, probabilistic feature selection algorithms have been proposed. Pure random walk entails mediocre performance in terms of search time. Introducing adaptiveness into a probabilistic algorithm can lead to a more focused search that results in a better search time. We compare two algorithms in search of an efficient but not myopic algorithm for feature selection. Based on the comparative study, we suggest some ways of improvement towards an evolutionary feature selection algorithm for data mining.

Original languageEnglish (US)
Title of host publicationProceedings of the 1999 Congress on Evolutionary Computation, CEC 1999
PublisherIEEE Computer Society
Pages1309-1313
Number of pages5
Volume2
DOIs
StatePublished - 1999
Externally publishedYes
Event1999 Congress on Evolutionary Computation, CEC 1999 - Washington, DC, United States
Duration: Jul 6 1999Jul 9 1999

Other

Other1999 Congress on Evolutionary Computation, CEC 1999
CountryUnited States
CityWashington, DC
Period7/6/997/9/99

ASJC Scopus subject areas

  • Computational Mathematics

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