Conformal Rule-Based Multi-label Classification
| Type of publication: | Inproceedings |
| Citation: | huellermeier20conformal |
| Booktitle: | KI 2020: Advances in Artificial Intelligence |
| Series: | Lecture Notes in Computer Science |
| Volume: | 12325 |
| Year: | 2020 |
| Month: | September |
| Publisher: | Springer, Cham |
| ISBN: | 978-3-030-58284-5 |
| URL: | https://arxiv.org/abs/2007.08145 |
| DOI: | 10.1007/978-3-030-58285-2_25 |
| 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. |
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