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An enhanced fuzzy min-max neural network for pattern classification
journal contributionposted on 2015-03-01, 00:00 authored by M F Mohammed, Chee Peng LimChee Peng Lim
An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification in this paper. The aim is to overcome a number of limitations of the original fuzzy min-max (FMM) network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible 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 various FMM-based models, support vector machine-based, Bayesian-based, decision tree-based, fuzzy-based, and neural-based classifiers. The empirical findings show that the newly introduced rules are able to realize EFMM as a useful model for undertaking pattern classification problems.
JournalIEEE Transactions on Neural Networks and Learning Systems
Pagination417 - 429
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication classificationC Journal article; C1 Refereed article in a scholarly journal
Copyright notice2015, IEEE
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Fuzzy min-max (FMM) modelhyperbox structureneural network learningpattern classification.Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Hardware & ArchitectureComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringpattern classificationFAULT-DETECTIONSYSTEMSIDENTIFICATIONARCHITECTURECLASSIFIERSPREDICTIONMACHINEMODELS