Consensus-Based Combining Method for Classifier Ensembles
Journal: The International Arab Journal of Information Technology (Vol.15, No. 1)Publication Date: 2018-01-01
Authors : Omar Alzubi Jafar Alzubi Sara Tedmori Hasan Rashaideh Omar Almomani;
Page : 76-86
Keywords : Artificial intelligence; classification; machine learning; pattern recognition; classifier ensembles; consensus theory; combining methods; majority voting; mean method; product method.;
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
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