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Consensus-Based Combining Method for Classifier Ensembles

Journal: The International Arab Journal of Information Technology (Vol.15, No. 1)

Publication Date:

Authors : ;

Page : 76-86

Keywords : Artificial intelligence; classification; machine learning; pattern recognition; classifier ensembles; consensus theory; combining methods; majority voting; mean method; product method.;

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Abstract

In this paper, a new method for combining an ensemble of classifiers, called Consensus-based Combining Method(CCM) is proposed and evaluated. As in most other combination methods, the outputs of multiple classifiers are weighted and summed together into a single final classification decision. However, unlike the other methods, CCM adjusts the weights iteratively after comparing all of the classifiers' outputs. Ultimately, all the weights converge to a final set of weights, and the combined output reaches a consensus. The effectiveness of CCM is evaluated by comparing it with popular linear combination methods (majority voting, product, and average method). Experiments are conducted on 14 public data sets, and on a blog spam data set created by the authors. Experimental results show that CCM provides a significant improvement in classification accuracy over the product and average methods. Moreover, results show that the CCM's classification accuracy is better than or comparable to that of majority voting

Last modified: 2019-04-29 18:47:51