A heuristic classifier ensemble for huge datasets
AMT'11 Proceedings of the 7th international conference on Active media technology
A scalable heuristic classifier for huge datasets: a theoretical approach
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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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.