[BibTeX] [RIS]
Conformal Rule-Based Multi-label Classification
Type of publication: Inproceedings
Citation: huellermeier20conformal
Booktitle: KI 2020: Advances in Artificial Intelligence
Series: Lecture Notes in Computer Science
Volume: 12325
Year: 2020
Month: September
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.
Authors Hüllermeier, Eyke
Fürnkranz, Johannes
Loza Mencía, Eneldo
Editors Schmid, Ute
Klügl, Franziska
Wolter, Diedrich