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

arXiv: https://arxiv.org/abs/1302.0540 · HTML: https://mlsanity.com/paper/1302.0540/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2013.

## Built on by (most cited first, 1 of 1)

- [Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review](https://mlsanity.com/paper/2311.06311/) (2023)

## Abstract (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.