Feature selection using ant colony optimization

  • Mohamed Deriche*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

Abstract

The ant feature selection algorithm has recently been proposed as a new method for feature subset selection. It uses measures of both local feature importance and overall performance of subsets to search the feature space for optimal solutions. In this paper, we evaluate the effect of different local importance measures; namely the Fisher Criterion, the Mutual Information based Feature Selection, and the Mutual Information Evaluation Function.

Original languageEnglish
Title of host publication2009 6th International Multi-Conference on Systems, Signals and Devices, SSD 2009
DOIs
StatePublished - 2009

Publication series

Name2009 6th International Multi-Conference on Systems, Signals and Devices, SSD 2009

Keywords

  • Ant colony optimization
  • Ant systems
  • Feature selection
  • Local measure

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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