A modified particle swarm optimization algorithm used for feature selection of UCI biomedical data sets

Khan, TA; Zain-Ul-Abideen, K; Ling, SH

A modified particle swarm optimization algorithm used for feature selection of UCI biomedical data sets

Khan, TA Zain-Ul-Abideen, K Ling, SH

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Publication Type:: Conference Proceeding
Citation:: 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019 - Proceedings, 2019
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Khan, TA	en_US
dc.contributor.author	Zain-Ul-Abideen, K	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019 - Proceedings, 2019	en_US
dc.identifier.isbn	9781728157092	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138224
dc.description.abstract	© 2019 IEEE. In biomedical and health care applications, classification examination is extensively used to help diagnose health problems, decision making and enhance standards of patient care. Feature selection is a significant data pre-processing method in classification problems. Training of the data is achieved by using a subset dataset from the UCI biomedical database. If the training dataset comprises inappropriate features, classification analysis resulted in inaccurate and incomprehensible results. In data mining, feature subset selection is data pre-processing phase that is of enormous importance. In this paper, for selecting a minimum number of features K-Nearest Neighbour (KNN) classifier is presented with a modified particle swarm optimization (MPSO) to obtain good classification precision. The proposed method is applied to three UCI medical data sets and is compared with other feature selection approach available in the literature. Results demonstrate that the feature subset recognized by the presented MPSO with KNN neighbor classifiers give better results and accuracy as compared to the other techniques.	en_US
dc.relation.ispartof	60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ITMS47855.2019.8940760	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A modified particle swarm optimization algorithm used for feature selection of UCI biomedical data sets	en_US
dc.type	Conference Proceeding
utslib.for	080201 Analysis of Algorithms and Complexity	en_US
utslib.for	0906 Electrical and Electronic Engineering	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	*
utslib.copyright.embargo	2021-10-01T00:00:00+1000
pubs.publication-status	Published	en_US

Abstract:

© 2019 IEEE. In biomedical and health care applications, classification examination is extensively used to help diagnose health problems, decision making and enhance standards of patient care. Feature selection is a significant data pre-processing method in classification problems. Training of the data is achieved by using a subset dataset from the UCI biomedical database. If the training dataset comprises inappropriate features, classification analysis resulted in inaccurate and incomprehensible results. In data mining, feature subset selection is data pre-processing phase that is of enormous importance. In this paper, for selecting a minimum number of features K-Nearest Neighbour (KNN) classifier is presented with a modified particle swarm optimization (MPSO) to obtain good classification precision. The proposed method is applied to three UCI medical data sets and is compared with other feature selection approach available in the literature. Results demonstrate that the feature subset recognized by the presented MPSO with KNN neighbor classifiers give better results and accuracy as compared to the other techniques.

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http://hdl.handle.net/10453/138224