A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min–max neural network. (February 2017)

• Record Type: Journal Article
• Authors: Mohammed, Mohammed Falah; Lim, Chee Peng
• Is Part Of: Neural networks. Volume 86(2017)
• Page Start: 69
• Page End: 79
• Publication Date: 2017-02
• Subjects: Fuzzy min–max model -- Pattern classification -- Hyperbox structure -- Neural network learning; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.10.012
• Languages: English
• ISSNs: 0893-6080

Abstract

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.