Fuzzy gaussian classifier for combining multiple learners

Ali F.; El Gayar N.; El Ola S.

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dc.contributor.author	Ali F.
dc.contributor.author	El Gayar N.
dc.contributor.author	El Ola S.
dc.contributor.other	Faculty of Computer Science
dc.contributor.other	October University for Modern Science and Arts
dc.contributor.other	6th October City
dc.contributor.other	Egypt; Faculty of Computers and Information
dc.contributor.other	Cairo University
dc.contributor.other	Giza
dc.contributor.other	Egypt; Center for Informatics Science
dc.contributor.other	School of Communication and Information Technology
dc.contributor.other	Nile University
dc.contributor.other	Giza
dc.contributor.other	Egypt
dc.date.accessioned	2020-01-25T19:58:33Z
dc.date.available	2020-01-25T19:58:33Z
dc.date.issued	2010
dc.identifier.isbn	978 970000000
dc.identifier.uri	https://t.ly/Ggx2E
dc.description	Scopus
dc.description.abstract	In 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.sponsorship	itida	en_US
dc.description.uri	https://www.scimagojr.com/journalsearch.php?q=19700174726&tip=sid&clean=0
dc.language.iso	English	en_US
dc.relation.ispartofseries	INFOS2010 - 2010 7th International Conference on Informatics and Systems
dc.subject	Classifier combination	en_US
dc.subject	Fuzzy gaussian classifier	en_US
dc.subject	Fuzzy K-nearest neighbors	en_US
dc.subject	K-nearest neighbors	en_US
dc.subject	Multi-layer perceptron	en_US
dc.subject	Benchmark data	en_US
dc.subject	Classification system	en_US
dc.subject	Classifier combination	en_US
dc.subject	Decision template	en_US
dc.subject	Fusion methods	en_US
dc.subject	Fuzzy integral	en_US
dc.subject	Fuzzy K-nearest neighbor classifier	en_US
dc.subject	Gaussian classifier	en_US
dc.subject	Gaussian model	en_US
dc.subject	K-nearest neighbor classifier	en_US
dc.subject	K-nearest neighbors	en_US
dc.subject	Multi layer perceptron	en_US
dc.subject	Multiple classifier systems	en_US
dc.subject	Product combinations	en_US
dc.subject	Fuzzy control	en_US
dc.subject	Gaussian distribution	en_US
dc.subject	Information science	en_US
dc.subject	Learning systems	en_US
dc.subject	Membership functions	en_US
dc.subject	Pattern recognition systems	en_US
dc.subject	Text processing	en_US
dc.subject	Classifiers	en_US
dc.title	Fuzzy gaussian classifier for combining multiple learners	en_US
dc.type	Conference Paper	en_US
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dcterms.source	Scopus
dc.Affiliation	October University for modern sciences and Arts (MSA)