On minimizing the position error in label ranking

Abstract

Conventional classification learning allows a classifier to make a one shot decision in order to identify the correct label. However, in many practical applications, the problem is not to give a single estimation, but to make repeated suggestions until the correct target label has been identified. Thus, the learner has to deliver a label ranking, that is, a ranking of all possible alternatives. In this paper, we discuss a loss function, called the position error, which is suitable for evaluating the performance of a label ranking algorithm in this setting. Moreover, we introduce "ranking through iterated choice", a general strategy for extending any multi-class classifier to this scenario, and propose an efficient implementation of this method by means of pairwise decomposition techniques. © Springer-Verlag Berlin Heidelberg 2007.