An interpretation of the boundary movement method for imbalanced dataset classification based on data quality

This paper describes how the classification of imbalanced datasets through support vector machines using the boundary movement method can be easily explained in terms of a cost-sensitive learning algorithm characterized by giving each example a cost in function of its class. Moreover, it is shown that under this interpretation the boundary movement is measured in terms of the squared norm of the separator’s slopes in feature space, thus providing practical insights in order to properly choose the boundary surface shift.