Feature ranking utilizing support vector machines´ SVs

Classification performance of different algorithms can often be improved by excluding irrelevant input features. This was the main motivation behind the significant number of studies proposing different families of feature selection techniques. The objective is to find a subset of features that can describe the input space, at least, as good as the original set of features. In this paper, we propose a hybrid method for feature ranking for support vector machines (SVMs); utilizing SVMs support vectors (SVs). The method first finds the subset of features that least contribute to interclass separation. These features are then re-ranked using correlation based feature selection algorithm, as a final step. Results on four benchmark medical data sets show that the proposed method, though simple, can be a promising feature reduction method for SVMs and other families of classifiers as well.