Enhanced feature selection algorithm using ant Colony Optimization and fuzzy memberships

Khushaba, RN; Alsukker, A; Al-Ani, A; Al-Jumaily, A

Enhanced feature selection algorithm using ant Colony Optimization and fuzzy memberships

Khushaba, RN

Alsukker, A Al-Ani, A

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 6th IASTED International Conference on Biomedical Engineering, BioMED 2008, 2008, pp. 34 - 39
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Alsukker, A	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings of the 6th IASTED International Conference on Biomedical Engineering, BioMED 2008, 2008, pp. 34 - 39	en_US
dc.identifier.isbn	9780889867215	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10849
dc.description.abstract	Feature selection is an indispensable pre-processing step when mining huge datasets that can significantly improve the overall system performance. This paper presents a novel feature selection method that utilizes both the Ant Colony Optimization (ACO) and fuzzy memberships. The algorithm estimates the local importance of subsets of features, i.e., their pheromone intensities by utilizing fuzzy c-means (FCM) clustering technique. In order to prove the effectiveness of the proposed method, a comparison with another powerful ACO based feature selection algorithm that utilizes the Mutual Information (MI) concept is presented. The method is tested on two biosignals driven applications: Brain Computer Interface (BCI), and prosthetic devices control with myoelectric signals (MES). A linear discriminant analysis (LDA) classifier is used to measure the performance of the selected subsets in both applications. Practical experiments prove that the new algorithm can be as accurate as the original method with MI, but with a significant reduction in computational cost, especially when dealing with huge datasets.	en_US
dc.relation.ispartof	Proceedings of the 6th IASTED International Conference on Biomedical Engineering, BioMED 2008	en_US
dc.title	Enhanced feature selection algorithm using ant Colony Optimization and fuzzy memberships	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Innsbruck, Austria	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Feature selection is an indispensable pre-processing step when mining huge datasets that can significantly improve the overall system performance. This paper presents a novel feature selection method that utilizes both the Ant Colony Optimization (ACO) and fuzzy memberships. The algorithm estimates the local importance of subsets of features, i.e., their pheromone intensities by utilizing fuzzy c-means (FCM) clustering technique. In order to prove the effectiveness of the proposed method, a comparison with another powerful ACO based feature selection algorithm that utilizes the Mutual Information (MI) concept is presented. The method is tested on two biosignals driven applications: Brain Computer Interface (BCI), and prosthetic devices control with myoelectric signals (MES). A linear discriminant analysis (LDA) classifier is used to measure the performance of the selected subsets in both applications. Practical experiments prove that the new algorithm can be as accurate as the original method with MI, but with a significant reduction in computational cost, especially when dealing with huge datasets.

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http://hdl.handle.net/10453/10849