Determination of an optimal feature selection method based on maximum Shapley value

We propose a novel feature selection methodology based on game theory. In this context, the players are the various feature selection methods and the characteristic function (payoff) represents the feature ranking agreement within a coalition of players. The Shapley value assigned to each feature selection method is computed and ranked from higher to lower. The best feature selection method is identified as the one having the highest Shapley value. Finally, we have performed a score fusion scheme using the Borda Count (BC) consensus function as a benchmark to the maximum-Shapley value proposed approach. In order to validate the results obtained experimentally, we have performed a classification using a set of UCI and Statlog datasets by invoking an SVM classifier. Experimental results demonstrate the efficiency of the proposed methodology compared to some state-of-the-art approaches.