A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification

At present, pattern classification is one of the most important aspects of establishing machine intelligence systems for tackling decision-making processes. The fuzzy min-max (FMM) neural network combines the operations of an artificial neural network and fuzzy set theory into a common framework. FM...

Full description

Bibliographic Details
Main Authors:	Alhroob, Essam, Mohammed, Mohammed Falah, Lim, Chee Peng, Tao, Hai
Format:	Article
Language:	English
Published:	IEEE 2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/25102/ http://umpir.ump.edu.my/id/eprint/25102/ http://umpir.ump.edu.my/id/eprint/25102/ http://umpir.ump.edu.my/id/eprint/25102/1/A%20Critical%20Review%20on%20Selected%20Fuzzy%20Min-Max.pdf

id	ump-25102
recordtype	eprints
spelling	ump-251022019-06-18T02:56:33Z http://umpir.ump.edu.my/id/eprint/25102/ A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification Alhroob, Essam Mohammed, Mohammed Falah Lim, Chee Peng Tao, Hai QA75 Electronic computers. Computer science At present, pattern classification is one of the most important aspects of establishing machine intelligence systems for tackling decision-making processes. The fuzzy min-max (FMM) neural network combines the operations of an artificial neural network and fuzzy set theory into a common framework. FMM is considered one of the most useful neural networks for pattern classification. This paper aims to 1) analyze the FMM neural network in terms of its impact in addressing pattern classification problems; 2) examine models that are proposed based on the original FMM model (i.e., existing FMM-based variants); 3) identify the challenges associated with FMM and its variants, and; 4) discuss future trends and make recommendations for improvement. The review is conducted based on a methodical protocol. Through a rigorous searching and filtering process, the relevant studies are extracted and comprehensively analyzed to adequately address the defined research questions. The findings indicate that FMM plays a critical role in providing solutions to pattern classification issues. The FMM model and a number of FMM-based variants are identified and systematically analyzed with respect to their aims, improvements introduced and results achieved. In addition, FMM and its variants are critically analyzed with respect to their benefits and limitations. This paper shows that the existing FMM-based variants still encounter issues in terms of the learning process (expansion, overlap test, and contraction), which influence the classification performance. Based on the review findings, research opportunities are suggested to propose a new model to enhance the number of existing FMM models, particularly in terms of their learning process by minimizing hyperbox overlap pertaining to different classes as well as avoiding membership ambiguity of the overlapped region. In short, this review provides a comprehensive and critical reference for researchers and practitioners to leverage FMM and its variants for undertaking pattern classification tasks. IEEE 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25102/1/A%20Critical%20Review%20on%20Selected%20Fuzzy%20Min-Max.pdf Alhroob, Essam and Mohammed, Mohammed Falah and Lim, Chee Peng and Tao, Hai (2019) A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification. IEEE Access, 7. pp. 56129-56146. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2019.2911955 https://doi.org/10.1109/ACCESS.2019.2911955
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Alhroob, Essam Mohammed, Mohammed Falah Lim, Chee Peng Tao, Hai A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification
description	At present, pattern classification is one of the most important aspects of establishing machine intelligence systems for tackling decision-making processes. The fuzzy min-max (FMM) neural network combines the operations of an artificial neural network and fuzzy set theory into a common framework. FMM is considered one of the most useful neural networks for pattern classification. This paper aims to 1) analyze the FMM neural network in terms of its impact in addressing pattern classification problems; 2) examine models that are proposed based on the original FMM model (i.e., existing FMM-based variants); 3) identify the challenges associated with FMM and its variants, and; 4) discuss future trends and make recommendations for improvement. The review is conducted based on a methodical protocol. Through a rigorous searching and filtering process, the relevant studies are extracted and comprehensively analyzed to adequately address the defined research questions. The findings indicate that FMM plays a critical role in providing solutions to pattern classification issues. The FMM model and a number of FMM-based variants are identified and systematically analyzed with respect to their aims, improvements introduced and results achieved. In addition, FMM and its variants are critically analyzed with respect to their benefits and limitations. This paper shows that the existing FMM-based variants still encounter issues in terms of the learning process (expansion, overlap test, and contraction), which influence the classification performance. Based on the review findings, research opportunities are suggested to propose a new model to enhance the number of existing FMM models, particularly in terms of their learning process by minimizing hyperbox overlap pertaining to different classes as well as avoiding membership ambiguity of the overlapped region. In short, this review provides a comprehensive and critical reference for researchers and practitioners to leverage FMM and its variants for undertaking pattern classification tasks.
format	Article
author	Alhroob, Essam Mohammed, Mohammed Falah Lim, Chee Peng Tao, Hai
author_facet	Alhroob, Essam Mohammed, Mohammed Falah Lim, Chee Peng Tao, Hai
author_sort	Alhroob, Essam
title	A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification
title_short	A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification
title_full	A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification
title_fullStr	A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification
title_full_unstemmed	A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification
title_sort	critical review on selected fuzzy min-max neural networks and their significance and challenges in pattern classification
publisher	IEEE
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25102/ http://umpir.ump.edu.my/id/eprint/25102/ http://umpir.ump.edu.my/id/eprint/25102/ http://umpir.ump.edu.my/id/eprint/25102/1/A%20Critical%20Review%20on%20Selected%20Fuzzy%20Min-Max.pdf
first_indexed	2023-09-18T22:38:22Z
last_indexed	2023-09-18T22:38:22Z
_version_	1777416748431048704

A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification

Similar Items