TY  - CONF
ID  - huellermeier20MLRLchallenges
T1  - Rule-Based Multi-label Classification: Challenges and Opportunities
A1  - Hüllermeier, Eyke
A1  - Fürnkranz, Johannes
A1  - Loza Mencía, Eneldo
A1  - Nguyen, Vu-Linh
A1  - Rapp, Michael
ED  - Gutiérrez-Basulto, Víctor
ED  - Kliegr, Tomás
ED  - Soylu, Ahmet
ED  - Giese, Martin
ED  - Roman, Dumitru
TI  - Rules and Reasoning
T3  - Lecture Notes in Computer Science
Y1  - 2020
VL  - 12173
SP  - 3
EP  - 19
PB  - Springer International Publishing
SN  - 978-3-030-57977-7
UR  - https://link.springer.com/chapter/10.1007/978-3-030-57977-7_1
M2  - doi: https://doi.org/10.1007/978-3-030-57977-7_1
KW  - multilabel classification
KW  - Rule Learning
N2  - In the context of multi-label classification (MLC), rule-based learning algorithms have a number of appealing properties that are not, at least not as a whole, shared by other approaches. This includes the potential interpretability of rules, their ability to model (local) label dependencies in a flexible way, and the facile customization of a predictor to different loss functions. In this paper, we present a modular framework for rule-based MLC and discuss related challenges and opportunities for multi-label rule learning.
ER  -