A New Hyperbox Selection Rule and a Pruning Strategy for the Enhanced Fuzzy Min–Max Neural Network

Mohammed, Mohammed Falah and Chee, Peng Lim (2017) A New Hyperbox Selection Rule and a Pruning Strategy for the Enhanced Fuzzy Min–Max Neural Network. Neural Networks, 86. pp. 69-79. ISSN 0893-6080. (Published)

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Fuzzy min–max model; Pattern classification; Hyperbox structure; Neural network learning
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty/Division:	Faculty of Computer System And Software Engineering
Depositing User:	Mrs. Neng Sury Sulaiman
Date Deposited:	26 Apr 2017 02:41
Last Modified:	12 Jan 2018 08:35
URI:	http://umpir.ump.edu.my/id/eprint/16555
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