On Generalized Fuzzy Jensen-Exponential Divergence and Its Application to Pattern Recognition

Verma, R; Merigó, JM; Sahni, M

On Generalized Fuzzy Jensen-Exponential Divergence and Its Application to Pattern Recognition

Verma, R Merigó, JM Sahni, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 1515-1519
Issue Date:: 2019-01-28

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Field	Value	Language
dc.contributor.author	Verma, R
dc.contributor.author	Merigó, JM
dc.contributor.author	Sahni, M
dc.date	2018-11-18
dc.date.accessioned	2020-05-31T22:07:56Z
dc.date.available	2020-05-31T22:07:56Z
dc.date.issued	2019-01-28
dc.identifier.citation	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 1515-1519
dc.identifier.isbn	9781538692769
dc.identifier.uri	http://hdl.handle.net/10453/141017
dc.description.abstract	© 2018 IEEE. This paper develops a novel information theoretic divergence measure between two fuzzy sets based on exponential function and applies it to solve pattern recognition problems. First, we generalize the idea of fuzzy Jensen-exponential divergence and propose a new parametric divergence called fuzzy Jensen-exponential divergence of order-α to measure the information of discrimination between two fuzzy sets. We also prove some properties of the proposed measure and discuss its particular cases. Finally, we apply the proposed divergence measure between fuzzy sets to deal with pattern recognition problems with fuzzy information.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
dc.relation.ispartof	2018 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.relation.isbasedon	10.1109/SSCI.2018.8628862
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	On Generalized Fuzzy Jensen-Exponential Divergence and Its Application to Pattern Recognition
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing
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 Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-05-31T22:07:48Z
pubs.finish-date	2018-11-21
pubs.publication-status	Published
pubs.start-date	2018-11-18
utslib.start-page	1515

Abstract:

© 2018 IEEE. This paper develops a novel information theoretic divergence measure between two fuzzy sets based on exponential function and applies it to solve pattern recognition problems. First, we generalize the idea of fuzzy Jensen-exponential divergence and propose a new parametric divergence called fuzzy Jensen-exponential divergence of order-α to measure the information of discrimination between two fuzzy sets. We also prove some properties of the proposed measure and discuss its particular cases. Finally, we apply the proposed divergence measure between fuzzy sets to deal with pattern recognition problems with fuzzy information.

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http://hdl.handle.net/10453/141017