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A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network

Mohammed, Mohammed Falah and Lim, Chee Peng 2017, A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network, Neural networks, vol. 86, pp. 69-79, doi: 10.1016/j.neunet.2016.10.012.

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Title A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network
Author(s) Mohammed, Mohammed Falah
Lim, Chee PengORCID iD for Lim, Chee Peng
Journal name Neural networks
Volume number 86
Start page 69
End page 79
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-02
ISSN 1879-2782
Keyword(s) Fuzzy min–max model
Hyperbox structure
Neural network learning
Pattern classification
Datasets as Topic
Fuzzy Logic
Neural Networks (Computer)
Summary In this paper, we extend our previous work on the Enhanced Fuzzy Min-Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data. The effectiveness of the proposed network is evaluated using a number of benchmark data sets. The results compare favorably with those from other related models. The findings indicate that the newly introduced hyperbox winner selection rule coupled with the pruning strategy are useful for undertaking pattern classification problems.
Language eng
DOI 10.1016/j.neunet.2016.10.012
Field of Research 080101 Adaptive Agents and Intelligent Robotics
MD Multidisciplinary
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
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Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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