Feature Selection for Classification Using an Ant System Approach

Abstract : Many applications such as pattern recognition and data mining 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, redundant or noisy features while keeping the most informative ones. In this paper, an ant system approach for solving feature selection for classification is presented. The results we got are promising in terms of the accuracy of the classifier and the number of selected features in all the used datasets.
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Mike Hinchey; Bernd Kleinjohann; Lisa Kleinjohann; Peter A. Lindsay; Franz J. Rammig; Jon Timmis; Marilyn Wolf. 7th IFIP TC 10 Working Conference on Distributed, Parallel and Biologically Inspired Systems (DIPES) / 3rd IFIP TC 10 International Conference on Biologically-Inspired Collaborative Computing (BICC) / Held as Part of World Computer Congress (WCC) , Sep 2010, Brisbane, Australia. Springer, IFIP Advances in Information and Communication Technology, AICT-329, pp.233-241, 2010, Distributed, Parallel and Biologically Inspired Systems. 〈10.1007/978-3-642-15234-4_23〉
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Nadia Abd-Alsabour. Feature Selection for Classification Using an Ant System Approach. Mike Hinchey; Bernd Kleinjohann; Lisa Kleinjohann; Peter A. Lindsay; Franz J. Rammig; Jon Timmis; Marilyn Wolf. 7th IFIP TC 10 Working Conference on Distributed, Parallel and Biologically Inspired Systems (DIPES) / 3rd IFIP TC 10 International Conference on Biologically-Inspired Collaborative Computing (BICC) / Held as Part of World Computer Congress (WCC) , Sep 2010, Brisbane, Australia. Springer, IFIP Advances in Information and Communication Technology, AICT-329, pp.233-241, 2010, Distributed, Parallel and Biologically Inspired Systems. 〈10.1007/978-3-642-15234-4_23〉. 〈hal-01054496〉

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