Attribute weighting via differential evolution for attribute weighted clonal selection algorithm

Wu, J; Cai, Z; Chen, X; Ao, S; Zhang, Y

Attribute weighting via differential evolution for attribute weighted clonal selection algorithm

Wu, J Cai, Z Chen, X Ao, S Zhang, Y

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Publication Type:: Journal Article
Citation:: Journal of Computers (Finland), 2012, 7 (12), pp. 3013 - 3019
Issue Date:: 2012-12-28

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Field	Value	Language
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Cai, Z	en_US
dc.contributor.author	Chen, X	en_US
dc.contributor.author	Ao, S	en_US
dc.contributor.author	Zhang, Y	en_US
dc.date.issued	2012-12-28	en_US
dc.identifier.citation	Journal of Computers (Finland), 2012, 7 (12), pp. 3013 - 3019	en_US
dc.identifier.issn	1796-203X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121531
dc.description.abstract	Classification is an important technology in data mining, while clonal selection algorithm (CSA) is a very effective classification method. Although CSA brings a new effective tool for solving complex problems, we can not completely say that it over-performs to other algorithms especially in the classification field. A main problem of CSA classifier is that it does not carry attribute imbalance. It uses a pure distance criterion to calculate affinity degree of the antibody and antigen. So we utilize weighting attribute scheme to balance the effects of attributes in classification process and attribute weighted CSA (AWCSA) comes into existence. The efficiency of AWCSA lies mainly in the attribute weighting scheme it uses. In this paper we use differential evolution (DE) algorithm to determine the weights of attributes and then use these weights in AWCSA. We evaluate the performance of new algorithm (DE-AWCSA) on six standard datasets. Experimental results show that this attribute weighting process highly benefits the classification accuracy. © 2012 Academy Publisher.	en_US
dc.relation.ispartof	Journal of Computers (Finland)	en_US
dc.relation.isbasedon	10.4304/jcp.7.12.3013-3019	en_US
dc.title	Attribute weighting via differential evolution for attribute weighted clonal selection algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

Classification is an important technology in data mining, while clonal selection algorithm (CSA) is a very effective classification method. Although CSA brings a new effective tool for solving complex problems, we can not completely say that it over-performs to other algorithms especially in the classification field. A main problem of CSA classifier is that it does not carry attribute imbalance. It uses a pure distance criterion to calculate affinity degree of the antibody and antigen. So we utilize weighting attribute scheme to balance the effects of attributes in classification process and attribute weighted CSA (AWCSA) comes into existence. The efficiency of AWCSA lies mainly in the attribute weighting scheme it uses. In this paper we use differential evolution (DE) algorithm to determine the weights of attributes and then use these weights in AWCSA. We evaluate the performance of new algorithm (DE-AWCSA) on six standard datasets. Experimental results show that this attribute weighting process highly benefits the classification accuracy. © 2012 Academy Publisher.

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http://hdl.handle.net/10453/121531