Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification

Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification

An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations...

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Main Authors: Mohammed, Mohammed Falah, Chee, Peng Lim
Format: Article
Language:English
Published: Elsevier Ltd 2017
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/16440/
http://umpir.ump.edu.my/id/eprint/16440/
http://umpir.ump.edu.my/id/eprint/16440/
http://umpir.ump.edu.my/id/eprint/16440/1/fskkp-2017-falah-Improving%20the%20fuzzy%20min-max1.pdf
id ump-16440
recordtype eprints
spelling ump-164402018-01-12T08:35:07Z http://umpir.ump.edu.my/id/eprint/16440/ Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification Mohammed, Mohammed Falah Chee, Peng Lim QA76 Computer software An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers. Elsevier Ltd 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16440/1/fskkp-2017-falah-Improving%20the%20fuzzy%20min-max1.pdf Mohammed, Mohammed Falah and Chee, Peng Lim (2017) Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification. Applied Soft Computing, 52. pp. 135-145. ISSN 1568-4946 (print); 1872-9681 (online) https://doi.org/10.1016/j.asoc.2016.12.001 DOI: 10.1016/j.asoc.2016.12.001
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Mohammed, Mohammed Falah
Chee, Peng Lim
Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification
description An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers.
format Article
author Mohammed, Mohammed Falah
Chee, Peng Lim
author_facet Mohammed, Mohammed Falah
Chee, Peng Lim
author_sort Mohammed, Mohammed Falah
title Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification
title_short Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification
title_full Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification
title_fullStr Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification
title_full_unstemmed Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification
title_sort improving the fuzzy min-max neural network with a k-nearest hyperbox expansion rule for pattern classification
publisher Elsevier Ltd
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/16440/
http://umpir.ump.edu.my/id/eprint/16440/
http://umpir.ump.edu.my/id/eprint/16440/
http://umpir.ump.edu.my/id/eprint/16440/1/fskkp-2017-falah-Improving%20the%20fuzzy%20min-max1.pdf
first_indexed 2023-09-18T22:22:07Z
last_indexed 2023-09-18T22:22:07Z
_version_ 1777415725894336512

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