Rule-Based Multi-label Classification: Challenges and Opportunities
| Type of publication: | Inproceedings |
| Citation: | huellermeier20MLRLchallenges |
| Booktitle: | Rules and Reasoning |
| Series: | Lecture Notes in Computer Science |
| Volume: | 12173 |
| Year: | 2020 |
| Month: | August |
| Pages: | 3--19 |
| Publisher: | Springer International Publishing |
| ISBN: | 978-3-030-57977-7 |
| URL: | https://link.springer.com/chapter/10.1007/978-3-030-57977-7_1 |
| DOI: | https://doi.org/10.1007/978-3-030-57977-7_1 |
| Abstract: | 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. |
| Keywords: | multilabel classification, Rule Learning |
| Authors | |
| Editors | |
|
Topics
|
|
|
|
