Feature selection for classification using an ant colony system

Nadia Abd-Alsabour*, Marcus Randall

*Corresponding author for this work

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

17 Citations (Scopus)

Abstract

Many applications such as pattern recognition require selecting a subset of the input features in order to represent the whole set of features. The aim of feature selection is to remove irrelevant or redundant features while keeping the most informative ones. In this paper, an ant colony system approach for solving feature selection for classification is presented. The proposed algorithm was tested using artificial and real-world datasets. The results are promising in terms of the accuracy of the classifier and the number of selected features in all the used datasets. The results of the proposed algorithm have been compared with other results available in the literature and found to be favorable.

Original languageEnglish
Title of host publicationProceedings - 6th IEEE International Conference on e-Science Workshops, e-ScienceW 2010
Pages86-91
Number of pages6
DOIs
Publication statusPublished - 2010
Event6th IEEE International Conference on e-Science Workshops, e-ScienceW 2010 - Brisbane, QLD, Australia
Duration: 7 Dec 201010 Dec 2010

Conference

Conference6th IEEE International Conference on e-Science Workshops, e-ScienceW 2010
CountryAustralia
CityBrisbane, QLD
Period7/12/1010/12/10

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  • Cite this

    Abd-Alsabour, N., & Randall, M. (2010). Feature selection for classification using an ant colony system. In Proceedings - 6th IEEE International Conference on e-Science Workshops, e-ScienceW 2010 (pp. 86-91). [5693146] https://doi.org/10.1109/eScienceW.2010.23