Dual support vector domain description for imbalanced classification

Abstract

As machine learning acquires special attention for real-world problem solving, a growing number of new problems not previously considered have appeared. One of such problems is the imbalance in class distributions, which is said to hinder the performance of traditional error-minimization-based classification algorithms. In this paper we propose an improved rule-based decision boundary for the Support Vector Domain Description that uses an additional nested classification unit to improve the accuracy of the outlier class, hence improving the overall performance of the classifier. Computer simulations show that the proposed strategy, which we have termed Dual Support Vector Domain Description, outperforms related literature approaches in several benchmark instances. © 2012 Springer-Verlag.