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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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/25103/ http://umpir.ump.edu.my/id/eprint/25103/1/Green%20sonochemical%20synthesis%20of%20few1.pdf |
| _version_ | 1848822200965005312 |
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| author | Alhroob, Essam Ngahzaifa, Ab. Ghani |
| author_facet | Alhroob, Essam Ngahzaifa, Ab. Ghani |
| author_sort | Alhroob, Essam |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:37:28Z |
| format | Conference or Workshop Item |
| id | ump-25103 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:37:28Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 (Published) https://doi.org/10.1109/ICCSCE.2018.8685029 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
| 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 |