Date of this Version

12-7-2010

Document Type

Conference Paper

Publication Details

Published Version.

Abd-Alsabour, N. & Randall, M. (2010). Feature selection for classification using an ant colony system. Paper presented at the Sixth IEEE international conference on e-Science: e-Science 2010, Brisbane, Australia.

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2010 HERDC submission. FoR Code: 080500

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ISBN

978-0-7695-4295-9

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 rising 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.

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This document has been peer reviewed.