A Weighted Voting Classifier Based on Differential Evolution

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.

[1]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[2]  Juan José Rodríguez Diez,et al.  A weighted voting framework for classifiers ensembles , 2012, Knowledge and Information Systems.

[3]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[4]  Ching Y. Suen,et al.  Application of majority voting to pattern recognition: an analysis of its behavior and performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[5]  Yang Wang,et al.  Repairing the crossover rate in adaptive differential evolution , 2014, Appl. Soft Comput..

[6]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[7]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[8]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[9]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[10]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[11]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[12]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[13]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[14]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[15]  Nikunj C. Oza,et al.  Online Ensemble Learning , 2000, AAAI/IAAI.

[16]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[17]  Swagatam Das,et al.  Cluster-based differential evolution with Crowding Archive for niching in dynamic environments , 2014, Inf. Sci..

[18]  Asif Ekbal,et al.  Weighted Vote-Based Classifier Ensemble for Named Entity Recognition: A Genetic Algorithm-Based Approach , 2011, TALIP.

[19]  Janez Brest,et al.  Self-adaptive differential evolution algorithm using population size reduction and three strategies , 2011, Soft Comput..

[20]  Wei Tang,et al.  Corrigendum to "Ensembling neural networks: Many could be better than all" [Artificial Intelligence 137 (1-2) (2002) 239-263] , 2010, Artif. Intell..