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       author = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
        title = {On Minimizing the Position Error in Label Ranking},
       number = {TUD-KE-2007-04},
         year = {2007},
  institution = {TU Darmstadt, Knowledge Engineering Group},
          url = {http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-2007-04.pdf},
     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 propose “ranking through iterated choice”, a general strategy for
extending any multi-class classifier to this scenario. Its basic idea is to reduce label ranking
to standard classification by successively predicting a most likely class label and retraining
a model on the remaining classes. We demonstrate empirically that this procedure does
indeed reduce the position error in comparison with a conventional approach that ranks
the classes according to their estimated probabilities. Besides, we also address the issue of
implementing ranking through iterated choice in a computationally efficient way.}