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Fuzzy Gaussian classifier for combining multiple learners

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3 Author(s)
Ali, F. ; Fac. of Comput. Sci., October Univ. for Modern Sci. & Arts, 6th October City, Egypt ; El Gayar, N. ; El Ola, S.

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 Nai¿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.

Published in:

Informatics and Systems (INFOS), 2010 The 7th International Conference on

Date of Conference:

28-30 March 2010