Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification
An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd
2017
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/16440/ http://umpir.ump.edu.my/id/eprint/16440/1/fskkp-2017-falah-Improving%20the%20fuzzy%20min-max1.pdf |
| _version_ | 1848820193507147776 |
|---|---|
| author | Mohammed, Mohammed Falah Chee, Peng Lim |
| author_facet | Mohammed, Mohammed Falah Chee, Peng Lim |
| author_sort | Mohammed, Mohammed Falah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers. |
| first_indexed | 2025-11-15T02:05:34Z |
| format | Article |
| id | ump-16440 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:05:34Z |
| publishDate | 2017 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-164402018-01-12T08:35:07Z http://umpir.ump.edu.my/id/eprint/16440/ Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification Mohammed, Mohammed Falah Chee, Peng Lim QA76 Computer software An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers. Elsevier Ltd 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16440/1/fskkp-2017-falah-Improving%20the%20fuzzy%20min-max1.pdf Mohammed, Mohammed Falah and Chee, Peng Lim (2017) Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification. Applied Soft Computing, 52. pp. 135-145. ISSN 1568-4946 (print); 1872-9681 (online). (Published) https://doi.org/10.1016/j.asoc.2016.12.001 DOI: 10.1016/j.asoc.2016.12.001 |
| spellingShingle | QA76 Computer software Mohammed, Mohammed Falah Chee, Peng Lim Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification |
| title | Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification |
| title_full | Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification |
| title_fullStr | Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification |
| title_full_unstemmed | Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification |
| title_short | Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification |
| title_sort | improving the fuzzy min-max neural network with a k-nearest hyperbox expansion rule for pattern classification |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/16440/ http://umpir.ump.edu.my/id/eprint/16440/ http://umpir.ump.edu.my/id/eprint/16440/ http://umpir.ump.edu.my/id/eprint/16440/1/fskkp-2017-falah-Improving%20the%20fuzzy%20min-max1.pdf |