Minimax Probability TSK Fuzzy System Classifier: A More Transparent and Highly Interpretable Classification Model

Deng, Z; Cao, L; Jiang, Y; Wang, S

Minimax Probability TSK Fuzzy System Classifier: A More Transparent and Highly Interpretable Classification Model

Deng, Z Cao, L

Jiang, Y Wang, S

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2015, 23 (4), pp. 813 - 826
Issue Date:: 2015-08-01

Closed Access

	Filename	Description	Size
	mi.pdf	Published Version	480.3 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Deng, Z	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Jiang, Y	en_US
dc.contributor.author	Wang, S	en_US
dc.date.issued	2015-08-01	en_US
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2015, 23 (4), pp. 813 - 826	en_US
dc.identifier.issn	1063-6706	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121745
dc.description.abstract	© 1993-2012 IEEE. When an intelligent model is used for medical diagnosis, it is desirable to have a high level of interpretability and transparent model reliability for users. Compared with most of the existing intelligence models, fuzzy systems have shown a distinctive advantage in their interpretabilities. However, how to determine the model reliability of a fuzzy system trained for a recognition task is still an unsolved problem at present. In this study, a minimax probability Takagi-Sugeno-Kang (TSK) fuzzy system classifier called MP-TSK-FSC is proposed to train a fuzzy system classifier and determine the model reliability simultaneously. For the proposed MP-TSK-FSC, a lower bound of correct classification can be presented to the users to characterize the reliability of the trained fuzzy classifier. Thus, the obtained classifier has the distinctive characteristics of both a high level of interpretability and transparent model reliability inherited from the fuzzy system and minimax probability learning strategy, respectively. Our experiments on synthetic datasets and several real-world datasets for medical diagnosis have confirmed the distinctive characteristics of the proposed method.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP130102691
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/TFUZZ.2014.2328014	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Minimax Probability TSK Fuzzy System Classifier: A More Transparent and Highly Interpretable Classification Model	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	23	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

© 1993-2012 IEEE. When an intelligent model is used for medical diagnosis, it is desirable to have a high level of interpretability and transparent model reliability for users. Compared with most of the existing intelligence models, fuzzy systems have shown a distinctive advantage in their interpretabilities. However, how to determine the model reliability of a fuzzy system trained for a recognition task is still an unsolved problem at present. In this study, a minimax probability Takagi-Sugeno-Kang (TSK) fuzzy system classifier called MP-TSK-FSC is proposed to train a fuzzy system classifier and determine the model reliability simultaneously. For the proposed MP-TSK-FSC, a lower bound of correct classification can be presented to the users to characterize the reliability of the trained fuzzy classifier. Thus, the obtained classifier has the distinctive characteristics of both a high level of interpretability and transparent model reliability inherited from the fuzzy system and minimax probability learning strategy, respectively. Our experiments on synthetic datasets and several real-world datasets for medical diagnosis have confirmed the distinctive characteristics of the proposed method.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121745