Improved 2-norm Based Fuzzy Least Squares Twin Support Vector Machine

Borah, P; Gupta, D; Prasad, M

Improved 2-norm Based Fuzzy Least Squares Twin Support Vector Machine

Borah, P 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. 412 - 419
Issue Date:: 2019-01-28

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Field	Value	Language
dc.contributor.author	Borah, P	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. 412 - 419	en_US
dc.identifier.isbn	9781538692769	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132452
dc.description.abstract	© 2018 IEEE. In order to reduce the higher training cost of support vector machine (SVM) and its sensitivity towards noise and outliers, two fuzzy based approaches are proposed in this paper. The proposed approaches are based on least squares twin support vector machine (LSTWSVM) and fuzzy support vector machine (FSVM). The effects of noise and outliers are reduced by assigning lower membership values to the data points which are away from the class centers. Further, 2-norm of the slack vectors of the LSTWSVM formulation is taken after multiplying to their respective diagonal matrices of the membership values to effectively utilize the fuzzy membership principle and to make the optimization problem strongly convex. Moreover, the proposed approaches solve linear equations instead of quadratic programming problems which help in training faster. The effectiveness of the proposed approaches are established by comparing the classification accuracies and training time with support vector machine, fuzzy support vector machine, twin support vector machine and least squares twin support vector machine.	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.2018.8628818	en_US
dc.title	Improved 2-norm Based Fuzzy Least Squares Twin Support Vector Machine	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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. In order to reduce the higher training cost of support vector machine (SVM) and its sensitivity towards noise and outliers, two fuzzy based approaches are proposed in this paper. The proposed approaches are based on least squares twin support vector machine (LSTWSVM) and fuzzy support vector machine (FSVM). The effects of noise and outliers are reduced by assigning lower membership values to the data points which are away from the class centers. Further, 2-norm of the slack vectors of the LSTWSVM formulation is taken after multiplying to their respective diagonal matrices of the membership values to effectively utilize the fuzzy membership principle and to make the optimization problem strongly convex. Moreover, the proposed approaches solve linear equations instead of quadratic programming problems which help in training faster. The effectiveness of the proposed approaches are established by comparing the classification accuracies and training time with support vector machine, fuzzy support vector machine, twin support vector machine and least squares twin support vector machine.

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http://hdl.handle.net/10453/132452