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Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification
journal contribution
posted on 2017-03-01, 00:00 authored by M F Mohammed, Chee Peng LimChee Peng LimAn improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers.
History
Journal
Applied soft computing journalVolume
52Pagination
135 - 145Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
1568-4946Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2016, ElsevierUsage metrics
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Keywords
Fuzzy min-max modelpattern classificationhyperbox structureneural network learningScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsComputer ScienceARCHITECTUREALGORITHMARTMAPMODELSInformation SystemsArtificial Intelligence and Image Processing
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