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.
An interpretation of the boundary movement method for imbalanced dataset classification based on data quality / D. Malchiodi (SMART INNOVATION, SYSTEMS AND TECHNOLOGIES). - In: Neural nets and surroundings : 22nd Italian workshop on neural nets, WIRN 2012 : may 17-19, Vietri sul Mare, Salerno, Italy / [a cura di] B. Apolloni, S. Bassis, A. Esposito, F.C. Morabito. - Berlin : Springer, 2013. - ISBN 9783642354663. - pp. 21-27 (( Intervento presentato al 22. convegno Italian Workshop on Neural Nets (WIRN) tenutosi a Vietri sul Mare nel 2012 [10.1007/978-3-642-35467-0_3].
An interpretation of the boundary movement method for imbalanced dataset classification based on data quality
D. MalchiodiPrimo
2013
Abstract
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.File | Dimensione | Formato | |
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