Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis

Gupta, D; Borah, P; Sharma, UM; Prasad, M

Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis

Gupta, D Borah, P Sharma, UM Prasad, M

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2022, 34, (14), pp. 11335-11345
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Gupta, D
dc.contributor.author	Borah, P
dc.contributor.author	Sharma, UM
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2023-03-21T03:04:01Z
dc.date.available	2023-03-21T03:04:01Z
dc.date.issued	2022-01-01
dc.identifier.citation	Neural Computing and Applications, 2022, 34, (14), pp. 11335-11345
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/167922
dc.description.abstract	This paper proposes a fuzzy-based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) to reduce the effect of the outliers presented in biomedical data. The proposed FLTPMSVM assigns the weights to each data sample on the basis of fuzzy membership values to reduce the effect of outliers. This paper also adopts the square of the 2-norm of slack variables to make the objective function more convex. The proposed FLTPMSVM solves simple linearly convergent iterative schemes instead of solving a pair of quadratic programming problems. No external toolbox is required for the proposed FLTPMSVM as compared to the other methods. To establish the applicability of the proposed FLTPMSVM in the area of biomedical data classification, numerical experiments are performed on several biomedical datasets. The proposed FLTPMSVM gives an improved generalization performance and reduced training cost as compared to support vector machine (SVM), twin support vector machine (TWSVM), fuzzy twin support vector machine (FTSVM), twin parametric-margin support vector machine (TPMSVM) and new fuzzy twin support vector machine (NFTSVM).
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-021-05866-2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis
dc.type	Journal Article
utslib.citation.volume	34
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
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 - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-21T03:04:00Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	34
utslib.citation.issue	14

Abstract:

This paper proposes a fuzzy-based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) to reduce the effect of the outliers presented in biomedical data. The proposed FLTPMSVM assigns the weights to each data sample on the basis of fuzzy membership values to reduce the effect of outliers. This paper also adopts the square of the 2-norm of slack variables to make the objective function more convex. The proposed FLTPMSVM solves simple linearly convergent iterative schemes instead of solving a pair of quadratic programming problems. No external toolbox is required for the proposed FLTPMSVM as compared to the other methods. To establish the applicability of the proposed FLTPMSVM in the area of biomedical data classification, numerical experiments are performed on several biomedical datasets. The proposed FLTPMSVM gives an improved generalization performance and reduced training cost as compared to support vector machine (SVM), twin support vector machine (TWSVM), fuzzy twin support vector machine (FTSVM), twin parametric-margin support vector machine (TPMSVM) and new fuzzy twin support vector machine (NFTSVM).

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http://hdl.handle.net/10453/167922