AFSNN: A Classification Algorithm Using Axiomatic Fuzzy Sets and Neural Networks

Duan, X; Wang, Y; Pedrycz, W; Liu, X; Wang, C; Li, Z

AFSNN: A Classification Algorithm Using Axiomatic Fuzzy Sets and Neural Networks

Duan, X Wang, Y Pedrycz, W

Liu, X Wang, C Li, Z

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2018, 26 (5), pp. 3151 - 3163
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Duan, X	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Pedrycz, W https://orcid.org/0000-0002-9335-9930	en_US
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Wang, C	en_US
dc.contributor.author	Li, Z	en_US
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2018, 26 (5), pp. 3151 - 3163	en_US
dc.identifier.issn	1063-6706	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129686
dc.description.abstract	© 1993-2012 IEEE. In this study, we present a comprehensible classifier AFSNN that embeds a new type of coherence membership function, which builds upon the theoretical findings of the axiomatic fuzzy set (AFS) theory into the hidden layer of neural network with random weights (NNRWs). Borrowing from the idea of NNRWs that employs the random initialization technique, the relation among attributes, simple concepts, and complex concepts are randomly determined. Complex concepts are generated through the combination of randomly selected simple concepts by AFS logic operation. The output weights of NNRWs are utilized to evaluate the confidence of each complex concept for every target class, which means that the feasibility of complex concepts for every class is determined analytically rather than through the tuning parameters of constraint conditions such as in conventional AFS-based classifiers. For the proposed method, compared to other neural-network-based classification methods, the fuzzy descriptions generated from complex concepts in hidden layer make classification result human understandable. We have experimented with several benchmark datasets and compared the results with other neural network-based classifiers. We show that our method outperforms Ensemble, EvRBFN, NNEP, LVQ, and iRProp+ in the seven out of ten datasets. The results show that the performance of AFSNN is competitive in terms of classification accuracy and the network shows a distinctive capability of providing explicit knowledge in the form of linguistic description.	en_US
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/TFUZZ.2017.2788875	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	AFSNN: A Classification Algorithm Using Axiomatic Fuzzy Sets and Neural Networks	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	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
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 1993-2012 IEEE. In this study, we present a comprehensible classifier AFSNN that embeds a new type of coherence membership function, which builds upon the theoretical findings of the axiomatic fuzzy set (AFS) theory into the hidden layer of neural network with random weights (NNRWs). Borrowing from the idea of NNRWs that employs the random initialization technique, the relation among attributes, simple concepts, and complex concepts are randomly determined. Complex concepts are generated through the combination of randomly selected simple concepts by AFS logic operation. The output weights of NNRWs are utilized to evaluate the confidence of each complex concept for every target class, which means that the feasibility of complex concepts for every class is determined analytically rather than through the tuning parameters of constraint conditions such as in conventional AFS-based classifiers. For the proposed method, compared to other neural-network-based classification methods, the fuzzy descriptions generated from complex concepts in hidden layer make classification result human understandable. We have experimented with several benchmark datasets and compared the results with other neural network-based classifiers. We show that our method outperforms Ensemble, EvRBFN, NNEP, LVQ, and iRProp+ in the seven out of ten datasets. The results show that the performance of AFSNN is competitive in terms of classification accuracy and the network shows a distinctive capability of providing explicit knowledge in the form of linguistic description.

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http://hdl.handle.net/10453/129686