Feature Selection Via Concave Minimization and Support Vector Machines

Computational comparison is made between two feature selection approaches for finding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the n features (dimensions) as possible. In the concave minimization approach [19,5] a separating plane is generated by minimizing a weighted sum of distances of misclassified points to two parallel planes that bound the sets and which determine the separating plane midway between them. Furthermore, the number of dimensions of the space used to determine the plane is minimized. In the support vector machine approach [27, 7, 1, 10, 24, 28], in addition to minimizing the weighted sum of distances of misclassified points to the bounding planes, we also maximize the distance between the two bounding planes that generate the separating plane. Computational results show that feature suppression is an indirect consequence of the support vector machine approach when an appropriate norm is used. Numerical tests on 6 public data sets show that classifiers trained by the concave minimization approach and those trained by a support vector machine have comparable 10-fold cross-validation correctness. However, in all data sets tested, the classifiers obtained by the concave minimization approach selected fewer problem features that those trained by a support vector machine