Novel data knowledge representation with TSK-type preprocessed collaborative fuzzy rule based system

Prasad, M; Er, MJ; Lin, CT; Prasad, OK; Mohanty, M; Singh, J

Novel data knowledge representation with TSK-type preprocessed collaborative fuzzy rule based system

Prasad, M

Er, MJ Lin, CT Prasad, OK Mohanty, M

Singh, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, 2015, pp. 14-21
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Er, MJ
dc.contributor.author	Lin, CT
dc.contributor.author	Prasad, OK
dc.contributor.author	Mohanty, M https://orcid.org/0000-0002-0258-4586
dc.contributor.author	Singh, J
dc.date	2015-12-07
dc.date.accessioned	2023-03-24T10:34:53Z
dc.date.available	2023-03-24T10:34:53Z
dc.date.issued	2015-01-01
dc.identifier.citation	Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, 2015, pp. 14-21
dc.identifier.isbn	9781479975600
dc.identifier.uri	http://hdl.handle.net/10453/168370
dc.description.abstract	A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of Takagi-Sugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within each-other. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only halve or less/more than the halve of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show that proposed method performs better than existing methods on some benchmark problems.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015
dc.relation.ispartof	IEEE Symposium Series Computational Intelligence
dc.relation.isbasedon	10.1109/SSCI.2015.13
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Novel data knowledge representation with TSK-type preprocessed collaborative fuzzy rule based system
dc.type	Conference Proceeding
utslib.location.activity	Cape Town, SOUTH AFRICA
utslib.for	0902 Automotive Engineering
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 Science
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
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	*
dc.date.updated	2023-03-24T10:34:52Z
pubs.finish-date	2015-12-10
pubs.publication-status	Published
pubs.start-date	2015-12-07

Abstract:

A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of Takagi-Sugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within each-other. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only halve or less/more than the halve of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show that proposed method performs better than existing methods on some benchmark problems.

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http://hdl.handle.net/10453/168370