In this paper, we present a new feature subset selection method that intends to optimize or preserve the performances of a decisional system in case of nonstationary perturbations or loss of information. A two-step process is proposed. First, multiple classifiers are created based on random subspace method, and an initial decision is obtained by combining all the classifiers according to a weighted voting rule. Then, we classify anew all the observations with a subset of these classifiers, chosen in function of the quality of their related feature subspaces. To illustrate this approach, the two-class textured image segmentation problem is considered. Our attention is focused on trying to determine the optimum feature subsets in order to improve the classification accuracy at the borders between two textures. Experimental results demonstrate the effectiveness of the proposed approach.
['Xiyan He', 'Pierre Beauseroy', 'André Smolarz']
Random feature subset selection in a nonstationary environment: Application to textured image segmentation