Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models

Fuzzy min–max (FMM) neural network is one of the most powerful models for pattern classification. Various models have been introduced based on FMM model to improve the classification performance. However, the misclassification of the contraction process is a crucial issue that has to be handled in F...

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Main Authors: Alhroob, Essam, Mohammed, Mohammed Falah, Al Sayaydeh, Osama Nayel, Hujainah, Fadhl, Ngahzaifa, Ab. Ghani
Format: Conference or Workshop Item
Language:English
Published: Springer International Publishing 2020
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/25535/
http://umpir.ump.edu.my/id/eprint/25535/
http://umpir.ump.edu.my/id/eprint/25535/1/Analysis%20on%20Misclassification%20in%20Existing.pdf
id ump-25535
recordtype eprints
spelling ump-255352020-01-20T03:06:36Z http://umpir.ump.edu.my/id/eprint/25535/ Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models Alhroob, Essam Mohammed, Mohammed Falah Al Sayaydeh, Osama Nayel Hujainah, Fadhl Ngahzaifa, Ab. Ghani QA Mathematics Fuzzy min–max (FMM) neural network is one of the most powerful models for pattern classification. Various models have been introduced based on FMM model to improve the classification performance. However, the misclassification of the contraction process is a crucial issue that has to be handled in FMM models to improve classification accuracy. Hence, this research aims to analyse the existence and execution procedure of addressing the misclassification of the contraction in the current FMM models. In this manner, practitioners and researchers are aided in selecting the convenient model that can address the misclassification of the contraction and improve the performance of models in producing accurate classification results. A total of 15 existing FMM models are identified and analysed in terms of the contraction problem. Results reveal that only five models can address the contraction misclassification problem. However, these models suffer from serious limitations, including the inability to detect all overlap cases, and increasing the network structure complexity. A new model is thus needed to address the specified limitations for increasing the pattern classification accuracy. Springer International Publishing 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25535/1/Analysis%20on%20Misclassification%20in%20Existing.pdf Alhroob, Essam and Mohammed, Mohammed Falah and Al Sayaydeh, Osama Nayel and Hujainah, Fadhl and Ngahzaifa, Ab. Ghani (2020) Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models. In: IRICT 2019: Emerging Trends in Intelligent Computing and Informatics, 22-23 September 2019 , Johor, Malaysia. pp. 270-278., 1073. ISBN 978-3-030-33582-3 https://doi.org/10.1007/978-3-030-33582-3_26
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA Mathematics
spellingShingle QA Mathematics
Alhroob, Essam
Mohammed, Mohammed Falah
Al Sayaydeh, Osama Nayel
Hujainah, Fadhl
Ngahzaifa, Ab. Ghani
Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models
description Fuzzy min–max (FMM) neural network is one of the most powerful models for pattern classification. Various models have been introduced based on FMM model to improve the classification performance. However, the misclassification of the contraction process is a crucial issue that has to be handled in FMM models to improve classification accuracy. Hence, this research aims to analyse the existence and execution procedure of addressing the misclassification of the contraction in the current FMM models. In this manner, practitioners and researchers are aided in selecting the convenient model that can address the misclassification of the contraction and improve the performance of models in producing accurate classification results. A total of 15 existing FMM models are identified and analysed in terms of the contraction problem. Results reveal that only five models can address the contraction misclassification problem. However, these models suffer from serious limitations, including the inability to detect all overlap cases, and increasing the network structure complexity. A new model is thus needed to address the specified limitations for increasing the pattern classification accuracy.
format Conference or Workshop Item
author Alhroob, Essam
Mohammed, Mohammed Falah
Al Sayaydeh, Osama Nayel
Hujainah, Fadhl
Ngahzaifa, Ab. Ghani
author_facet Alhroob, Essam
Mohammed, Mohammed Falah
Al Sayaydeh, Osama Nayel
Hujainah, Fadhl
Ngahzaifa, Ab. Ghani
author_sort Alhroob, Essam
title Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models
title_short Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models
title_full Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models
title_fullStr Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models
title_full_unstemmed Analysis on Misclassification in Existing Contraction of Fuzzy Min–max Models
title_sort analysis on misclassification in existing contraction of fuzzy min–max models
publisher Springer International Publishing
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/25535/
http://umpir.ump.edu.my/id/eprint/25535/
http://umpir.ump.edu.my/id/eprint/25535/1/Analysis%20on%20Misclassification%20in%20Existing.pdf
first_indexed 2023-09-18T22:39:15Z
last_indexed 2023-09-18T22:39:15Z
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