An Improved Online Learning Algorithm for General Fuzzy Min-Max Neural Network

Khuat, TT; Chen, F; Gabrys, B

An Improved Online Learning Algorithm for General Fuzzy Min-Max Neural Network

Khuat, TT Chen, F

Gabrys, B

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-9
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Khuat, TT
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.date	2020-07-19
dc.date.accessioned	2021-03-03T03:58:41Z
dc.date.available	2021-03-03T03:58:41Z
dc.date.issued	2020-07-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-9
dc.identifier.isbn	9781728169262
dc.identifier.uri	http://hdl.handle.net/10453/146690
dc.description.abstract	© 2020 IEEE. This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max (GFMM) neural network to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new on-line learning algorithm, a simple ensemble method is also proposed.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	2020 International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/IJCNN48605.2020.9207534
dc.rights	© 2020 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	An Improved Online Learning Algorithm for General Fuzzy Min-Max Neural Network
dc.type	Conference Proceeding
utslib.citation.volume	00
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
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-03T03:58:39Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	00

Abstract:

© 2020 IEEE. This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max (GFMM) neural network to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new on-line learning algorithm, a simple ensemble method is also proposed.

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http://hdl.handle.net/10453/146690