Feature Selection With Intelligent Dynamic Swarm And Rough Set

Bae, C; Yeh, W; Chung, YY; Liu, S

Feature Selection With Intelligent Dynamic Swarm And Rough Set

Bae, C Yeh, W Chung, YY Liu, S

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Publisher:: Pergamon-Elsevier Science Ltd
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2010, 37 (10), pp. 7026 - 7032
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Bae, C	en_US
dc.contributor.author	Yeh, W	en_US
dc.contributor.author	Chung, YY	en_US
dc.contributor.author	Liu, S	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Expert Systems with Applications, 2010, 37 (10), pp. 7026 - 7032	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14551
dc.description.abstract	Data mining is the most commonly used name to solve problems by analyzing data already present in databases. Feature selection is an important problem in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets. Many approaches, methods and goals including Genetic Algorithms (GA) and swarm-based approaches have been tried out for feature selection in order to these goals. Furthermore, a new technique which named Particle Swarm Optimization (PSO) has been proved to be competitive with GA in several tasks, mainly in optimization areas. However, there are some shortcomings in PSO such as premature convergence. To overcome these, we propose a new evolutionary algorithm called Intelligent Dynamic Swarm (IDS) that is a modified Particle Swarm Optimization. Experimental results states competitive performance of IDS. Due to less computing for swarm generation, averagely IDS is over 30% faster than traditional PSO.	en_US
dc.publisher	Pergamon-Elsevier Science Ltd	en_US
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	10.1016/j.eswa.2010.03.016	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Feature Selection With Intelligent Dynamic Swarm And Rough Set	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	37	en_US
utslib.location.activity	ISI:000279408200034	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	ISI:000279408200034	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 Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	10	en_US
pubs.volume	37	en_US

Abstract:

Data mining is the most commonly used name to solve problems by analyzing data already present in databases. Feature selection is an important problem in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets. Many approaches, methods and goals including Genetic Algorithms (GA) and swarm-based approaches have been tried out for feature selection in order to these goals. Furthermore, a new technique which named Particle Swarm Optimization (PSO) has been proved to be competitive with GA in several tasks, mainly in optimization areas. However, there are some shortcomings in PSO such as premature convergence. To overcome these, we propose a new evolutionary algorithm called Intelligent Dynamic Swarm (IDS) that is a modified Particle Swarm Optimization. Experimental results states competitive performance of IDS. Due to less computing for swarm generation, averagely IDS is over 30% faster than traditional PSO.

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http://hdl.handle.net/10453/14551