ID  - huellermeier20conformal
T1  - Conformal Rule-Based Multi-label Classification
A1  - Hüllermeier, Eyke
A1  - Fürnkranz, Johannes
A1  - Loza Mencía, Eneldo
ED  - Schmid, Ute
ED  - Klügl, Franziska
ED  - Wolter, Diedrich
TI  - KI 2020: Advances in Artificial Intelligence
T3  - Lecture Notes in Computer Science
Y1  - 2020
VL  - 12325
PB  - Springer, Cham
SN  - 978-3-030-58284-5
UR  - https://arxiv.org/abs/2007.08145
M2  - doi: 10.1007/978-3-030-58285-2_25
N2  - 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.
ER  -