Fuzzy gaussian classifier for combining multiple learners

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dc.AffiliationOctober University for modern sciences and Arts (MSA)
dc.contributor.authorAli F.
dc.contributor.authorEl Gayar N.
dc.contributor.authorEl Ola S.
dc.contributor.otherFaculty of Computer Science
dc.contributor.otherOctober University for Modern Science and Arts
dc.contributor.other6th October City
dc.contributor.otherEgypt; Faculty of Computers and Information
dc.contributor.otherCairo University
dc.contributor.otherGiza
dc.contributor.otherEgypt; Center for Informatics Science
dc.contributor.otherSchool of Communication and Information Technology
dc.contributor.otherNile University
dc.contributor.otherGiza
dc.contributor.otherEgypt
dc.date.accessioned2020-01-25T19:58:33Z
dc.date.available2020-01-25T19:58:33Z
dc.date.issued2010
dc.descriptionScopus
dc.description.abstractIn the field of pattern recognition multiple classifier systems based on the combination of outputs from different classifiers have been proposed as a method of high performance classification systems. The objective of this work is to develop a fuzzy Gaussian classifier for combining multiple learners, we use a fuzzy Gaussian model to combine the outputs obtained from K-nearest neighbor classifier (KNN), Fuzzy K-nearest neighbor classifier and Multi-layer Perceptron (MLP) and then compare the results with Fuzzy Integral, Decision Templates, Weighted Majority, Majority Na�ve Bayes, Maximum, Minimum, Average and Product combination methods. Results on two benchmark data sets show that the proposed fusion method outperforms a wide variety of existing classifier combination methods.en_US
dc.description.sponsorshipitidaen_US
dc.description.urihttps://www.scimagojr.com/journalsearch.php?q=19700174726&tip=sid&clean=0
dc.identifier.isbn978 970000000
dc.identifier.urihttps://t.ly/Ggx2E
dc.language.isoEnglishen_US
dc.relation.ispartofseriesINFOS2010 - 2010 7th International Conference on Informatics and Systems
dc.subjectClassifier combinationen_US
dc.subjectFuzzy gaussian classifieren_US
dc.subjectFuzzy K-nearest neighborsen_US
dc.subjectK-nearest neighborsen_US
dc.subjectMulti-layer perceptronen_US
dc.subjectBenchmark dataen_US
dc.subjectClassification systemen_US
dc.subjectClassifier combinationen_US
dc.subjectDecision templateen_US
dc.subjectFusion methodsen_US
dc.subjectFuzzy integralen_US
dc.subjectFuzzy K-nearest neighbor classifieren_US
dc.subjectGaussian classifieren_US
dc.subjectGaussian modelen_US
dc.subjectK-nearest neighbor classifieren_US
dc.subjectK-nearest neighborsen_US
dc.subjectMulti layer perceptronen_US
dc.subjectMultiple classifier systemsen_US
dc.subjectProduct combinationsen_US
dc.subjectFuzzy controlen_US
dc.subjectGaussian distributionen_US
dc.subjectInformation scienceen_US
dc.subjectLearning systemsen_US
dc.subjectMembership functionsen_US
dc.subjectPattern recognition systemsen_US
dc.subjectText processingen_US
dc.subjectClassifiersen_US
dc.titleFuzzy gaussian classifier for combining multiple learnersen_US
dc.typeConference Paperen_US
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