Fuzzy gaussian classifier for combining multiple learners
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Date
2010
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Type
Conference Paper
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Series Info
INFOS2010 - 2010 7th International Conference on Informatics and Systems
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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.
Description
Scopus
Keywords
Classifier combination, Fuzzy gaussian classifier, Fuzzy K-nearest neighbors, K-nearest neighbors, Multi-layer perceptron, Benchmark data, Classification system, Classifier combination, Decision template, Fusion methods, Fuzzy integral, Fuzzy K-nearest neighbor classifier, Gaussian classifier, Gaussian model, K-nearest neighbor classifier, K-nearest neighbors, Multi layer perceptron, Multiple classifier systems, Product combinations, Fuzzy control, Gaussian distribution, Information science, Learning systems, Membership functions, Pattern recognition systems, Text processing, Classifiers