title: A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network.
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.
authors.affiliation: School of Computer Systems & Software Engineering, University Malaysia Pahang, 26300 Gambang, Pahang, Malaysia. Electronic address: falah@ump.edu.my.
authors.forename: Mohammed Falah
authors.initials: MF
authors.lastname: Mohammed
authors.affiliation: Institute for Intelligent Systems Research and Innovation, Deakin University, Australia.
authors.forename: Chee Peng
authors.initials: CP
authors.lastname: Lim
mesh_terms: D066264:Datasets as Topic / Q000145:classification*; D017143:Fuzzy Logic; D016571:Neural Networks, Computer
keywords: Fuzzy min–max model; Hyperbox structure; Neural network learning; Pattern classification