Conformal Rule-Based Multi-label Classification

Hüllermeier, Eyke ; Fürnkranz, Johannes und Loza Mencia, Eneldo (2020): Conformal Rule-Based Multi-label Classification. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on AI, Bamberg, Germany, September 21–25, 2020. In: KI 2020: Advances in Artificial Intelligence, Bd. 12325 Cham: Springer. S. 290-296

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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Publikationsform: Publisher's Version
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
• ISSN: 0302-9743
• Ort: Cham
• Sprache: Englisch