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

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• 作者：Mohammed Falah Mohammed^a ; ^{falah@ump.edu.my} ; Chee Peng Lim^b
• 关键词：Fuzzy min&ndash ; max model ; Pattern classification ; Hyperbox structure ; Neural network learning
• 刊名：Neural Networks
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：86
• 期：Complete
• 页码：69-79
• 全文大小：1645 K
• 卷排序：86

文摘

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.