Ali F.El Gayar N.El Ola S.Faculty of Computer ScienceOctober University for Modern Science and Arts6th October CityEgypt; Faculty of Computers and InformationCairo UniversityGizaEgypt; Center for Informatics ScienceSchool of Communication and Information TechnologyNile UniversityGizaEgypt2020-01-252020-01-252010978 970000000https://t.ly/Ggx2EScopusIn 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.EnglishClassifier combinationFuzzy gaussian classifierFuzzy K-nearest neighborsK-nearest neighborsMulti-layer perceptronBenchmark dataClassification systemClassifier combinationDecision templateFusion methodsFuzzy integralFuzzy K-nearest neighbor classifierGaussian classifierGaussian modelK-nearest neighbor classifierK-nearest neighborsMulti layer perceptronMultiple classifier systemsProduct combinationsFuzzy controlGaussian distributionInformation scienceLearning systemsMembership functionsPattern recognition systemsText processingClassifiersConference Paper