A support vector method for ranking minimizing the number of swapped pairs

Abstract

Learning tasks where the set Y of classes has an ordering relation arise in a number of important application fields. In this context, the loss function may be de- fined in different ways, ranging from multiclass classification to ordinal or metric regression. However, to consider only the ordered structure of Y , a measure of goodness of a hypothesis h has to be related to the number of pairs whose relative ordering is swapped by h. In this paper, we present a method, based on the use of a multivariate version of Support Vector Machines (SVM) that learns to order minimizing the number of swapped pairs. Finally, using benchmark datasets, we compare the scores so achieved with those found by other alternative approaches