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Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification

Mohammed, Mohammed Falah and Lim, Chee Peng 2017, Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification, Applied soft computing journal, vol. 52, pp. 135-145, doi: 10.1016/j.asoc.2016.12.001.

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Title Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification
Author(s) Mohammed, Mohammed Falah
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Journal name Applied soft computing journal
Volume number 52
Start page 135
End page 145
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-03
ISSN 1568-4946
Keyword(s) Fuzzy min-max model
pattern classification
hyperbox structure
neural network learning
Summary 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.
Language eng
DOI 10.1016/j.asoc.2016.12.001
Field of Research 0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
0806 Information Systems
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30093345

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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