Feature selection by Universum embedding

Feature selection in classification is an important task in machine learning. Inspired by the success of Universum support vector machine proposed by Weston et al. on improving the classification ability of classical support vector machine, this paper considers a special type of Universum and further lets it play its role in both useful feature identification and separating hyperplane construction, aiming to improve both the feature selection ability and classification performance of Universum support vector machine. By introducing this special Universum, a redundant feature can be identified by observing whether some Universum sample is useful. In fact, we prove that by observing the dual solution of the optimization problem, useful features can be selected from a set satisfying some properties. Due to the introduction of these extra Universum samples, it needs to cope with a large-scale optimization problem. To improve the training efficiency, we modify the sequential minimal optimization algorithm and further combine it with the coordinate descent technique to solve the proposed model. Experimental results on artificial datasets, benchmark datasets, and text classification datasets demonstrate that the proposed method improves the classification performance of support vector machine and Universum support vector machine, and also has good feature selection ability.