• Corpus ID: 15331432

A game-theoretic framework for classifier ensembles using weighted majority voting with local accuracy estimates

@article{Georgiou2013AGF,
  title={A game-theoretic framework for classifier ensembles using weighted majority voting with local accuracy estimates},
  author={Harris V. Georgiou and Michael E. Mavroforakis},
  journal={ArXiv},
  year={2013},
  volume={abs/1302.0540}
}

• H. Georgiou, M. Mavroforakis
• Published 3 February 2013
• Computer Science
• ArXiv

In this paper, a novel approach for the optimal combination of binary classifiers is proposed. The classifier combination problem is approached from a Game Theory perspective. The proposed framework of adapted weighted majority rules (WMR) is tested against common rank-based, Bayesian and simple majority models, as well as two soft-output averaging rules. Experiments with ensembles of Support Vector Machines (SVM), Ordinary Binary Tree Classifiers (OBTC) and weighted k-nearest-neighbor (w/k-NN) models on benchmark datasets indicate that this new adaptive WMR model, employing local accuracy estimators and the analytically computed optimal weights outperform all the other simple combination rules.