Takagi-Sugeno-Kang type collaborative fuzzy rule based system

Chou, KP; Prasad, M; Lin, YY; Joshi, S; Lin, CT; Chang, JY

Takagi-Sugeno-Kang type collaborative fuzzy rule based system

Chou, KP Prasad, M

Lin, YY Joshi, S

Lin, CT Chang, JY

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings, 2015, pp. 315-320
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Chou, KP
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Lin, YY
dc.contributor.author	Joshi, S https://orcid.org/0000-0003-4748-5001
dc.contributor.author	Lin, CT
dc.contributor.author	Chang, JY
dc.date	2014-12-09
dc.date.accessioned	2023-03-24T10:33:48Z
dc.date.available	2023-03-24T10:33:48Z
dc.date.issued	2015-01-01
dc.identifier.citation	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings, 2015, pp. 315-320
dc.identifier.isbn	9781479945191
dc.identifier.uri	http://hdl.handle.net/10453/168369
dc.description.abstract	© 2014 IEEE. In this paper, a Takagi-Sugeno-Kang (TSK) type collaborative fuzzy rule based system is proposed with the help of knowledge learning ability of collaborative fuzzy clustering (CFC). The proposed method split a huge dataset into several small datasets and applying collaborative mechanism to interact each other and this process could be helpful to solve the big data issue. The proposed method applies the collective knowledge of CFC as input variables and the consequent part is a linear combination of the input variables. Through the intensive experimental tests on prediction problem, the performance of the proposed method is as higher as other methods. The proposed method only uses one half information of given dataset for training process and provide an accurate modeling platform while other methods use whole information of given dataset for training.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings
dc.relation.ispartof	IEEE Symposium on Computational Intelligence and Data Mining
dc.relation.isbasedon	10.1109/CIDM.2014.7008684
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Takagi-Sugeno-Kang type collaborative fuzzy rule based system
dc.type	Conference Proceeding
utslib.location.activity	USA
utslib.for	0801 Artificial Intelligence and Image Processing
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	2023-03-24T10:33:47Z
pubs.finish-date	2014-12-12
pubs.publication-status	Published
pubs.start-date	2014-12-09

Abstract:

© 2014 IEEE. In this paper, a Takagi-Sugeno-Kang (TSK) type collaborative fuzzy rule based system is proposed with the help of knowledge learning ability of collaborative fuzzy clustering (CFC). The proposed method split a huge dataset into several small datasets and applying collaborative mechanism to interact each other and this process could be helpful to solve the big data issue. The proposed method applies the collective knowledge of CFC as input variables and the consequent part is a linear combination of the input variables. Through the intensive experimental tests on prediction problem, the performance of the proposed method is as higher as other methods. The proposed method only uses one half information of given dataset for training process and provide an accurate modeling platform while other methods use whole information of given dataset for training.

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http://hdl.handle.net/10453/168369