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.
Item Type: | Conference or Workshop Item (Paper) |
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Form of publication: | Publisher's Version |
Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems |
ISSN: | 0302-9743 |
Place of Publication: | Cham |
Language: | English |
Item ID: | 92525 |
Date Deposited: | 16. Feb 2023, 15:33 |
Last Modified: | 16. Feb 2023, 15:33 |