An Enhanced Fuzzy Min–Max Neural Network for Pattern Classification - 2015
An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification during this paper. The aim is to beat a number of limitations of the original fuzzy min-max (FMM) network and improve its classification performance. The key contributions are 3 heuristic rules to reinforce the learning algorithm of FMM. Initial, a new hyperbox expansion rule to eliminate the overlapping downside throughout the hyperbox enlargement method is urged. Second, the present hyperbox overlap test rule is extended to discover other doable overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. Efficacy of EFMM is evaluated using benchmark data sets and a real medical diagnosis task. The results are better than those from varied FMM-primarily based models, support vector machine-based mostly, Bayesian-based, call tree-based, fuzzy-based mostly, and neural-based mostly classifiers. The empirical findings show that the newly introduced rules are in a position to realize EFMM as a useful model for enterprise pattern classification issues.
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