Kernel Target Alignment based Fuzzy Least Square Twin Bounded Support Vector Machine

Gupta, U; Gupta, D; Prasad, M

Kernel Target Alignment based Fuzzy Least Square Twin Bounded Support Vector Machine

Gupta, U Gupta, D Prasad, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 228 - 235
Issue Date:: 2019-01-28

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Field	Value	Language
dc.contributor.author	Gupta, U	en_US
dc.contributor.author	Gupta, D	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.date.issued	2019-01-28	en_US
dc.identifier.citation	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 228 - 235	en_US
dc.identifier.isbn	9781538692769	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132457
dc.description.abstract	© 2018 IEEE. A kernel-target alignment based fuzzy least square twin bounded support vector machine (KTAFLSTBSVM) is proposed to reduce the effects of outliers and noise. The proposed model is an effective and efficient fuzzy based least square twin bounded support vector machine for binary classification where the membership values are assigned based on kernel-target alignment approach. The proposed KTA-FLSTBSVM solves the two systems of linear equations, which is computationally very fast with significant comparable performance. To development the robust model, this approach minimizes the structural risk which is the gist of statistical learning theory. This powerful KTA-FLSTBSVM approach is tested on artificial data sets as well as benchmark real-world datasets that provide significantly better result in terms of generalization performance and computational time.	en_US
dc.relation.ispartof	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018	en_US
dc.relation.isbasedon	10.1109/SSCI.2018.8628903	en_US
dc.title	Kernel Target Alignment based Fuzzy Least Square Twin Bounded Support Vector Machine	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. A kernel-target alignment based fuzzy least square twin bounded support vector machine (KTAFLSTBSVM) is proposed to reduce the effects of outliers and noise. The proposed model is an effective and efficient fuzzy based least square twin bounded support vector machine for binary classification where the membership values are assigned based on kernel-target alignment approach. The proposed KTA-FLSTBSVM solves the two systems of linear equations, which is computationally very fast with significant comparable performance. To development the robust model, this approach minimizes the structural risk which is the gist of statistical learning theory. This powerful KTA-FLSTBSVM approach is tested on artificial data sets as well as benchmark real-world datasets that provide significantly better result in terms of generalization performance and computational time.

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http://hdl.handle.net/10453/132457