A flexible enhanced fuzzy min-max neural network for pattern classification
In this paper, the existing enhanced fuzzy min–max (EFMM) neural network is improved with a flexible learning procedure for undertaking pattern classification tasks. Four new contributions are introduced. Firstly, a new training strategy is proposed for avoiding the generation of unnecessary overlap...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
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Elsevier
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41060/ http://umpir.ump.edu.my/id/eprint/41060/1/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network_ABST.pdf http://umpir.ump.edu.my/id/eprint/41060/2/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network%20for%20pattern%20classification.pdf |
| _version_ | 1848826224204316672 |
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| author | Alhroob, Essam Mohammed, Mohammed Falah Al Sayaydeh, Osama Nayel Hujainah, Fadhl Ngahzaifa, Ab Ghani Lim, Chee Peng |
| author_facet | Alhroob, Essam Mohammed, Mohammed Falah Al Sayaydeh, Osama Nayel Hujainah, Fadhl Ngahzaifa, Ab Ghani Lim, Chee Peng |
| author_sort | Alhroob, Essam |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | In this paper, the existing enhanced fuzzy min–max (EFMM) neural network is improved with a flexible learning procedure for undertaking pattern classification tasks. Four new contributions are introduced. Firstly, a new training strategy is proposed for avoiding the generation of unnecessary overlapped regions between hyperboxes of different classes. The learning phase is simplified by eliminating the contraction procedure. Secondly, a new flexible expansion procedure is introduced. It eliminates the use of a user-defined parameter (expansion coefficient) to determine the hyperbox sizes. Thirdly, a new overlap test rule is applied during the test phase to identify the containment cases and activate the contraction procedure (if necessary). Fourthly, a new contraction procedure is formulated to overcome the containment cases and avoid the data distortion problem. Both the third and fourth contributions are important for preventing the catastrophic forgetting issue and supporting the stability-plasticity principle pertaining to online learning. The performance of the proposed model is evaluated with benchmark data sets. The results demonstrate its efficiency in handling pattern classification tasks, outperforming other related models in online learning environments. |
| first_indexed | 2025-11-15T03:41:25Z |
| format | Article |
| id | ump-41060 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:41:25Z |
| publishDate | 2024 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-410602024-04-24T06:20:24Z http://umpir.ump.edu.my/id/eprint/41060/ A flexible enhanced fuzzy min-max neural network for pattern classification Alhroob, Essam Mohammed, Mohammed Falah Al Sayaydeh, Osama Nayel Hujainah, Fadhl Ngahzaifa, Ab Ghani Lim, Chee Peng QA75 Electronic computers. Computer science In this paper, the existing enhanced fuzzy min–max (EFMM) neural network is improved with a flexible learning procedure for undertaking pattern classification tasks. Four new contributions are introduced. Firstly, a new training strategy is proposed for avoiding the generation of unnecessary overlapped regions between hyperboxes of different classes. The learning phase is simplified by eliminating the contraction procedure. Secondly, a new flexible expansion procedure is introduced. It eliminates the use of a user-defined parameter (expansion coefficient) to determine the hyperbox sizes. Thirdly, a new overlap test rule is applied during the test phase to identify the containment cases and activate the contraction procedure (if necessary). Fourthly, a new contraction procedure is formulated to overcome the containment cases and avoid the data distortion problem. Both the third and fourth contributions are important for preventing the catastrophic forgetting issue and supporting the stability-plasticity principle pertaining to online learning. The performance of the proposed model is evaluated with benchmark data sets. The results demonstrate its efficiency in handling pattern classification tasks, outperforming other related models in online learning environments. Elsevier 2024 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41060/1/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/41060/2/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network%20for%20pattern%20classification.pdf Alhroob, Essam and Mohammed, Mohammed Falah and Al Sayaydeh, Osama Nayel and Hujainah, Fadhl and Ngahzaifa, Ab Ghani and Lim, Chee Peng (2024) A flexible enhanced fuzzy min-max neural network for pattern classification. Expert Systems with Applications, 251 (124030). pp. 1-13. ISSN 0957-4174. (Published) https://doi.org/10.1016/j.eswa.2024.124030 https://doi.org/10.1016/j.eswa.2024.124030 |
| spellingShingle | QA75 Electronic computers. Computer science Alhroob, Essam Mohammed, Mohammed Falah Al Sayaydeh, Osama Nayel Hujainah, Fadhl Ngahzaifa, Ab Ghani Lim, Chee Peng A flexible enhanced fuzzy min-max neural network for pattern classification |
| title | A flexible enhanced fuzzy min-max neural network for pattern classification |
| title_full | A flexible enhanced fuzzy min-max neural network for pattern classification |
| title_fullStr | A flexible enhanced fuzzy min-max neural network for pattern classification |
| title_full_unstemmed | A flexible enhanced fuzzy min-max neural network for pattern classification |
| title_short | A flexible enhanced fuzzy min-max neural network for pattern classification |
| title_sort | flexible enhanced fuzzy min-max neural network for pattern classification |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/41060/ http://umpir.ump.edu.my/id/eprint/41060/ http://umpir.ump.edu.my/id/eprint/41060/ http://umpir.ump.edu.my/id/eprint/41060/1/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network_ABST.pdf http://umpir.ump.edu.my/id/eprint/41060/2/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network%20for%20pattern%20classification.pdf |