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@ARTICLE{ln:MlcAbstention,
    author = {Nguyen, Vu-Linh and H{\"{u}}llermeier, Eyke},
     month = {Apr.},
     title = {Reliable Multilabel Classification: Prediction with Partial Abstention},
   journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
    volume = {34},
    number = {04},
      year = {2020},
     pages = {5264-5271},
       url = {https://ojs.aaai.org/index.php/AAAI/article/view/5972},
       doi = {10.1609/aaai.v34i04.5972},
  abstract = {In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. We propose a formalization of MLC with abstention in terms of a generalized loss minimization problem and present first results for the case of the Hamming loss, rank loss, and F-measure, both theoretical and experimental.}
}