An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification

An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification

An ensemble of Enhanced Fuzzy Min Max (EFMM) neural networks for data classification is proposed in this paper. The certified belief in strength (CBS) method is used to formulate the ensemble EFMM model, with the aim to improve the performance of individual EFMM networks. The CBS method is used to...

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Main Authors: Mohammed, Mohammed Falah, Rassem, Taha H.
Format: Article
Language:English
English
Published: Universitas Ahmad Dahlan 2017
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/18180/
http://umpir.ump.edu.my/id/eprint/18180/
http://umpir.ump.edu.my/id/eprint/18180/
http://umpir.ump.edu.my/id/eprint/18180/1/An%20Ensemble%20of%20Enhanced%20Fuzzy%20Min%20Max%20Neural%20Networks%20for%20Data%20Classification.pdf
http://umpir.ump.edu.my/id/eprint/18180/2/An%20Ensemble%20of%20Enhanced%20Fuzzy%20Min%20Max%20Neural%20Networks%20for%20Data%20Classification%201.pdf
id ump-18180
recordtype eprints
spelling ump-181802018-01-12T08:34:44Z http://umpir.ump.edu.my/id/eprint/18180/ An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification Mohammed, Mohammed Falah Rassem, Taha H. QA76 Computer software An ensemble of Enhanced Fuzzy Min Max (EFMM) neural networks for data classification is proposed in this paper. The certified belief in strength (CBS) method is used to formulate the ensemble EFMM model, with the aim to improve the performance of individual EFMM networks. The CBS method is used to measure trustworthiness of each individual EFMM network based on its reputation and strength indicators. Trust is built from strong elements associated with the EFMM network, allowing the CBS method to improve the performance of the ensemble model. An auction procedure based on the first-price sealed-bid scheme is adopted for determining the winning EFMM network in undertaking classification tasks. The effectiveness of the ensemble model is demonstrated using a number of benchmark data sets. Comparing with the existing EFMM networks, the proposed ensemble model is able to improve classification accuracy rates in the empirical study. Universitas Ahmad Dahlan 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18180/1/An%20Ensemble%20of%20Enhanced%20Fuzzy%20Min%20Max%20Neural%20Networks%20for%20Data%20Classification.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/18180/2/An%20Ensemble%20of%20Enhanced%20Fuzzy%20Min%20Max%20Neural%20Networks%20for%20Data%20Classification%201.pdf Mohammed, Mohammed Falah and Rassem, Taha H. (2017) An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification. Telkomnika, 15 (2). pp. 942-948. ISSN 1693-6930 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/6149 DOI: 10.12928/TELKOMNIKA.v15i2.6149
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic QA76 Computer software
spellingShingle QA76 Computer software
Mohammed, Mohammed Falah
Rassem, Taha H.
An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification
description An ensemble of Enhanced Fuzzy Min Max (EFMM) neural networks for data classification is proposed in this paper. The certified belief in strength (CBS) method is used to formulate the ensemble EFMM model, with the aim to improve the performance of individual EFMM networks. The CBS method is used to measure trustworthiness of each individual EFMM network based on its reputation and strength indicators. Trust is built from strong elements associated with the EFMM network, allowing the CBS method to improve the performance of the ensemble model. An auction procedure based on the first-price sealed-bid scheme is adopted for determining the winning EFMM network in undertaking classification tasks. The effectiveness of the ensemble model is demonstrated using a number of benchmark data sets. Comparing with the existing EFMM networks, the proposed ensemble model is able to improve classification accuracy rates in the empirical study.
format Article
author Mohammed, Mohammed Falah
Rassem, Taha H.
author_facet Mohammed, Mohammed Falah
Rassem, Taha H.
author_sort Mohammed, Mohammed Falah
title An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification
title_short An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification
title_full An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification
title_fullStr An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification
title_full_unstemmed An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification
title_sort ensemble of enhanced fuzzy min max neural networks for data classification
publisher Universitas Ahmad Dahlan
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/18180/
http://umpir.ump.edu.my/id/eprint/18180/
http://umpir.ump.edu.my/id/eprint/18180/
http://umpir.ump.edu.my/id/eprint/18180/1/An%20Ensemble%20of%20Enhanced%20Fuzzy%20Min%20Max%20Neural%20Networks%20for%20Data%20Classification.pdf
http://umpir.ump.edu.my/id/eprint/18180/2/An%20Ensemble%20of%20Enhanced%20Fuzzy%20Min%20Max%20Neural%20Networks%20for%20Data%20Classification%201.pdf
first_indexed 2023-09-18T22:25:36Z
last_indexed 2023-09-18T22:25:36Z
_version_ 1777415945069789184

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