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Abstract and Applied Analysis
Volume 2014, Article ID 376950, 6 pages
Research Article

A Weighted Voting Classifier Based on Differential Evolution

School of Computer and Information Technology, Liaoning Normal University, Dalian 116081, China

Received 8 April 2014; Accepted 12 May 2014; Published 22 May 2014

Academic Editor: Caihong Li

Copyright © 2014 Yong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier. Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution. After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule. Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality.