On the Advantages of Weighted L1-Norm Support Vector Learning for Unbalanced Binary Classification Problems

In this paper we analyze support vector machine classification using the soft margin approach that allows for errors and margin violations during the training stage. Two models for learning the separating hyperplane do exist. We study the behavior of the optimization algorithms in terms of training characteristics and test accuracy for unbalanced data sets. The main goal of our work is to compare the features of the resulting classification functions, which are mainly defined by the support vectors arising during the support vector machine training