The combination of multiple classifiers is shown to be suitable for improving the performance of pattern recognition systems. The theoretical and experimental results in the literature clearly show that combining multiple classifiers is only effective if the individual classifiers are accurate and diverse. Selecting a suitable combination method and the diversity of an ensemble of classifiers are known to be important key issues in constructing a good ensemble system. The combination method should be selected so that the classifiers complement each other. In this paper we review diversity creation methods including implicit and explicit methods. Also a review on combination methods is presented that covers the following rules: maximum, minimum, mean, product, voting, Bayesian, fuzzy integral, decision template and Dempster-Shafer.
Kabir,E. and Hassan Nabavi-kerizi,S. (2005). Classifier Combination: Diversity Creation and Combination Methods. (e215976). The CSI Journal on Computer Science and Engineering, 3(3), e215976
MLA
Kabir,E. , and Hassan Nabavi-kerizi,S. . "Classifier Combination: Diversity Creation and Combination Methods" .e215976 , The CSI Journal on Computer Science and Engineering, 3, 3, 2005, e215976.
HARVARD
Kabir E., Hassan Nabavi-kerizi S. (2005). 'Classifier Combination: Diversity Creation and Combination Methods', The CSI Journal on Computer Science and Engineering, 3(3), e215976.
CHICAGO
E. Kabir and S. Hassan Nabavi-kerizi, "Classifier Combination: Diversity Creation and Combination Methods," The CSI Journal on Computer Science and Engineering, 3 3 (2005): e215976,
VANCOUVER
Kabir E., Hassan Nabavi-kerizi S. Classifier Combination: Diversity Creation and Combination Methods. CSIonJCSE, 2005; 3(3): e215976.
The CSI Journal on Computer Science and Engineering