A New Approach to Improve the Vote-Based Classifier Selection
Hamïd Parvïn, Hosein Alizadeh, Behrouz Minaei Bidgoli · 2008
In the past decade many new methods were proposed for combining multiple classifiers. Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. We propose a GA-based method for constructing a neural network ensemble using a weighted vote-based classifier selection approach. Main presumption of this method is that the reliability of the predictions of each classifier differs among classes. During testing, the classifiers whose votes are considered as being reliable are combined using weighted majority voting. This method of combination outperforms the ensemble of all classifiers almost 2.26% and 4.00% on Hoda and Wine data sets, respectively.