Synchronized feature selection for Support Vector Machines with twin hyperplanes

In this work, a novel feature selection method for twin Support Vector Machine (SVM) is presented. The main idea is to combine two regularizers, namely the Euclidean and infinite norm to perform twin classification and variable selection simultaneously. This latter task is performed in a coordinated fashion, enabling that the same attributes are selected in each twin classifiers. A single optimization problem is used to solve both subproblems, leading to a sparse final classification rule. Experiments on low- and high-dimensional datasets indicate that our approaches present the best average performance compared to well-known feature selection strategies, also achieving a synchronized feature elimination in the two twin classifiers. Our approaches are also able to improve the performance of the twin classifier, demonstrating the importance of feature selection in high-dimensional tasks.