Labelwise versus Pairwise Decomposition in Label Ranking

  title={Labelwise versus Pairwise Decomposition in Label Ranking},
  author={Weiwei Cheng and Sascha Henzgen and Eyke H{\"u}llermeier},
Label ranking is a specific type of preference learning problem, namely the problem of learning a model that maps instances to rankings over a finite set of predefined alternatives (labels). State-of-the-art approaches to label ranking include decomposition techniques that reduce the original problem to binary classification; ranking by pairwise comparison (RPC), for example, constructs one binary problem for each pair of alternatives. In general, each classification example refers to the… CONTINUE READING


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