Logo Logo
Switch Language to German
Hüllermeier, Eyke ORCID: 0000-0002-9944-4108; Fürnkranz, Johannes; Loza Mencia, Eneldo (2020): Conformal Rule-Based Multi-label Classification. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on AI, September 21–25, 2020, Bamberg, Germany.
Full text not available from 'Open Access LMU'.


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.