Conformal Rule-Based Multi-label Classification

Eyke Hüllermeier; Johannes Fürnkranz; Eneldo Loza Mencia

doi:10.1007/978-3-030-58285-2_25

Conformal Rule-Based Multi-label Classification

Machine Learning 2020-12-09 v1 Machine Learning

Authors: Eyke Hüllermeier , Johannes Fürnkranz , Eneldo Loza Mencia

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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.

Keywords

multi-label classification conformal prediction calibration

Cite

@article{arxiv.2007.08145,
  title  = {Conformal Rule-Based Multi-label Classification},
  author = {Eyke Hüllermeier and Johannes Fürnkranz and Eneldo Loza Mencia},
  journal= {arXiv preprint arXiv:2007.08145},
  year   = {2020}
}

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