Coalition game theory based feature subset selection for hyperspectral image classification

In this paper, an algorithm to select feature subsets for hyper-spectral image classification using the principle of coalition game theory is presented. The feature selection algorithms associated with non-linear kernel based Support Vector Machines (SVM) are either NP-hard or greedy and hence, not very optimal. To deal with this problem, a metric based on the principles of coalition game theory called Shapely value and a sampling approximation is used to determine the contribution of a subset of features towards the classification task. Starting with a few subsets of features, we successively partition each of them into smaller parts if the smaller parts contribute more than a pre-determined threshold compared to the parent subset. The algorithm is terminated when the subsets of features do not change from one iteration to the next. The final subsets of features are then used in multiple kernels and sparse weights of these kernels are optimally learned to build a maximum margin classifier. The algorithm is applied on real hyperspectral datasets and the results are presented in the paper.