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     author = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes and Loza Menc{\'{\i}}a, Eneldo},
     editor = {Schmid, Ute and Kl{\"{u}}gl, Franziska and Wolter, Diedrich},
      month = sep,
      title = {Conformal Rule-Based Multi-label Classification},
  booktitle = {KI 2020: Advances in Artificial Intelligence},
     series = {Lecture Notes in Computer Science},
     volume = {12325},
       year = {2020},
  publisher = {Springer, Cham},
       isbn = {978-3-030-58284-5},
        url = {https://arxiv.org/abs/2007.08145},
        doi = {10.1007/978-3-030-58285-2_25},
   abstract = {We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rules have the ability to provide natural (non-)conformity scores, which are required by CP, while CP suggests a way to calibrate the assessment of candidate rules, thereby supporting better predictions and more elaborate decision making. We illustrate the potential usefulness of calibrated conformity scores in a case study on lazy multi-label rule learning.}