Fuzzy gaussian classifier for combining multiple learners

Farid Ali*, Neamat El Gayar, Sanaa El Ola

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 Naïve 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.

Original languageEnglish
Title of host publication2010 The 7th International Conference on Informatics and Systems (INFOS)
ISBN (Electronic)9781424458288
ISBN (Print)9781424458288
Publication statusPublished - 6 May 2010
Event7th International Conference on Informatics and Systems 2010 - Cairo, Egypt
Duration: 28 Mar 201030 Mar 2010


Conference7th International Conference on Informatics and Systems 2010
Abbreviated titleINFOS 2010


  • Classifier combination
  • Fuzzy gaussian classifier
  • Fuzzy K-nearest neighbors
  • K-nearest neighbors
  • Multi-layer perceptron

ASJC Scopus subject areas

  • Information Systems


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