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In this paper, we introduce a novel semi-random subspace sampling for classification (for short, denoted by FS_RS). In this method, a ranking feature list is obtained by using feature selection first, and then the more important N0 features in the front of the ranking feature list are chosen, and N1 features is randomly selected from the remaining features in the ranking feature list. Along this sampling...
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