A comparative study of general fuzzy min-max neural networks for pattern classification problems

Khuat, TT; Gabrys, B

A comparative study of general fuzzy min-max neural networks for pattern classification problems

Khuat, TT

Gabrys, B

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2020, 386 pp. 110 - 125
Issue Date:: 2020-04-21

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Field	Value	Language
dc.contributor.author	Khuat, TT https://orcid.org/0000-0002-6456-8530	en_US
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846	en_US
dc.date.issued	2020-04-21	en_US
dc.identifier.citation	Neurocomputing, 2020, 386 pp. 110 - 125	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138318
dc.description.abstract	© 2019 Elsevier B.V. General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental learning and agglomerative learning. This paper presents a comprehensive empirical study of performance influencing factors, advantages, and drawbacks of the general fuzzy min-max neural network on pattern classification problems. The subjects of this study include (1) the impact of maximum hyperbox size, (2) the influence of the similarity threshold and measures on the agglomerative learning algorithm, (3) the effect of data presentation order, (4) comparative performance evaluation of the GFMM with other types of fuzzy min-max neural networks and prevalent machine learning algorithms. The experimental results on benchmark datasets widely used in machine learning showed overall strong and weak points of the GFMM classifier. These outcomes also informed potential research directions for this class of machine learning algorithms in the future.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2019.12.090	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A comparative study of general fuzzy min-max neural networks for pattern classification problems	en_US
dc.type	Journal Article
utslib.citation.volume	386	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	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
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-12-01T00:00:00+1100
pubs.publication-status	Accepted	en_US
pubs.volume	386	en_US

Abstract:

© 2019 Elsevier B.V. General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental learning and agglomerative learning. This paper presents a comprehensive empirical study of performance influencing factors, advantages, and drawbacks of the general fuzzy min-max neural network on pattern classification problems. The subjects of this study include (1) the impact of maximum hyperbox size, (2) the influence of the similarity threshold and measures on the agglomerative learning algorithm, (3) the effect of data presentation order, (4) comparative performance evaluation of the GFMM with other types of fuzzy min-max neural networks and prevalent machine learning algorithms. The experimental results on benchmark datasets widely used in machine learning showed overall strong and weak points of the GFMM classifier. These outcomes also informed potential research directions for this class of machine learning algorithms in the future.

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http://hdl.handle.net/10453/138318