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.

This paper proposes a k-Nearest Neighbor (k-NN) based scheme in order to update a change detection decision from a Feed-Forward Neural Network (FFNN). Change and no-change detection is treated as a context-free binary classification problem, using a FFNN fed by pixel spectral intensity data. The particularity of this method, compared to the existing and established ones, is that it takes into account the result of change/no-change decision of neighboring pixels during the detection process. A first stage FFNN pixel-based classification is conducted and the output change/no-change label assigned to a pixel is updated via information from neighboring pixel labels. A majority vote strategy is adopted within the k-nearest neighbors' labels. Experiments are performed on real optical aerial images with large time differences. We show that the proposed system produces an overall performance detection improvement of around 13% and 4% of F-measure and G-mean values, respectively, over the FFNN baseline system.

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