Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution

Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution

The fuzzy min-max (FMM) neural network is one of the most powerful neural networks that combines neural network and fuzzy set theory into a common framework for tackling pattern classification problems. FMM neural network carries out learning processes that consist of hyperbox expansion, hyperbox ov...

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Main Authors: Alhroob, Essam, Ngahzaifa, Ab. Ghani
Format: Conference or Workshop Item
Language:English
Published: IEEE 2018
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/25103/
http://umpir.ump.edu.my/id/eprint/25103/
http://umpir.ump.edu.my/id/eprint/25103/1/Green%20sonochemical%20synthesis%20of%20few1.pdf
id ump-25103
recordtype eprints
spelling ump-251032019-06-18T04:05:59Z http://umpir.ump.edu.my/id/eprint/25103/ Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution Alhroob, Essam Ngahzaifa, Ab. Ghani QA75 Electronic computers. Computer science The fuzzy min-max (FMM) neural network is one of the most powerful neural networks that combines neural network and fuzzy set theory into a common framework for tackling pattern classification problems. FMM neural network carries out learning processes that consist of hyperbox expansion, hyperbox overlap test and hyperbox contraction to execute pattern classification. Although these processes provide FMM with several outstanding features and make it a unique pattern classifier, the contraction process is considered a major limitation that affects the FMM learning process and hinders it from handling hyperbox overlapped boundaries appropriately. This drawback could affect membership decision making and cause the classifier to provide random decisions when test samples have the same fitness values with different hyperboxes from different classes (ambiguity issue). The performance of the classifier consequently declines. Thus, this study aims to provide a conceptual solution called `fuzzy min-max classifier based on new membership function' through a new method, `Euclidean distance', in the test phase to handle the hyperbox overlapping boundaries of different classes. The conceptual solution has not been implemented and tested in a real scenario. Hence, the application of the conceptual solution to real scenarios is recommended in future studies to assess its performance. IEEE 2018 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25103/1/Green%20sonochemical%20synthesis%20of%20few1.pdf Alhroob, Essam and Ngahzaifa, Ab. Ghani (2018) Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution. In: 8th IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2018), 23-25 November 2018 , Penang, Malaysia. pp. 131-135.. ISBN 978-1-5386-6324-0 https://doi.org/10.1109/ICCSCE.2018.8685029
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
Ngahzaifa, Ab. Ghani
Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution
description The fuzzy min-max (FMM) neural network is one of the most powerful neural networks that combines neural network and fuzzy set theory into a common framework for tackling pattern classification problems. FMM neural network carries out learning processes that consist of hyperbox expansion, hyperbox overlap test and hyperbox contraction to execute pattern classification. Although these processes provide FMM with several outstanding features and make it a unique pattern classifier, the contraction process is considered a major limitation that affects the FMM learning process and hinders it from handling hyperbox overlapped boundaries appropriately. This drawback could affect membership decision making and cause the classifier to provide random decisions when test samples have the same fitness values with different hyperboxes from different classes (ambiguity issue). The performance of the classifier consequently declines. Thus, this study aims to provide a conceptual solution called `fuzzy min-max classifier based on new membership function' through a new method, `Euclidean distance', in the test phase to handle the hyperbox overlapping boundaries of different classes. The conceptual solution has not been implemented and tested in a real scenario. Hence, the application of the conceptual solution to real scenarios is recommended in future studies to assess its performance.
format Conference or Workshop Item
author Alhroob, Essam
Ngahzaifa, Ab. Ghani
author_facet Alhroob, Essam
Ngahzaifa, Ab. Ghani
author_sort Alhroob, Essam
title Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution
title_short Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution
title_full Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution
title_fullStr Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution
title_full_unstemmed Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution
title_sort fuzzy min-max classifier based on new membership function for pattern classification: a conceptual solution
publisher IEEE
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/25103/
http://umpir.ump.edu.my/id/eprint/25103/
http://umpir.ump.edu.my/id/eprint/25103/1/Green%20sonochemical%20synthesis%20of%20few1.pdf
first_indexed 2023-09-18T22:38:22Z
last_indexed 2023-09-18T22:38:22Z
_version_ 1777416748577849344

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