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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 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)

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Abstract

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.

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: 16 Jun 2017 03:11
Last Modified: 12 Jan 2018 08:35
URI: http://umpir.ump.edu.my/id/eprint/16440
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