On Minimizing the Position Error in Label Ranking

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On Minimizing the Position Error in Label Ranking
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.
Eyke Hüllermeier, Johannes Fürnkranz
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ECML
Authors Eyke Hüllermeier, Johannes Fürnkranz
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