Hüllermeier, Eyke ![]() |
Full text not available from 'Open Access LMU'.
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.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Form of publication: | Publisher's Version |
Published in: | Lecture Notes in Computer Science, Vol. 12325 pp. 290-296 |
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 |
ID Code: | 92519 |
Deposited On: | 09. Sep 2022 11:21 |
Last Modified: | 09. Sep 2022 11:21 |
Repository Staff Only: item control page