Selection of the linearly separable feature subsets

Abstract

We address a situation when more than one feature subset allows for linear separability of given data sets. Such situation can occur if a small number of cases is represented in a highly dimensional feature space. The method of the feature selection based on minimisation of a special criterion function is here analysed. This criterion function is convex and piecewise-linear (CPL). The proposed method allows to evaluate different feature subsets enabling linear separability and to choose the best one among them. A comparison of this method with the Support Vector Machines is also included.