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Effect of local measure on performance of the ant feature selection algorithm

  • Ahmed AI-Ani*
  • , Mohamed Deriche
  • *Corresponding author for this work

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

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 publication2005 1st International Conference on Computers, Communications and Signal Processing with Special Track on Biomedical Engineering, CCSP 2005
Pages139-143
Number of pages5
DOIs
StatePublished - 2005

Publication series

Name2005 1st International Conference on Computers, Communications and Signal Processing with Special Track on Biomedical Engineering, CCSP 2005

Keywords

  • Ant colony optimization
  • Ant systems
  • Local measure
  • Selection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Biomedical Engineering

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