Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-58285-2_25 | KI |
DocType | ISSN | Citations |
Conference | Draft of an article presented at KI 2020, 43. German Conference on
Artificial Intelligence, Bamberg, Germany | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eyke Hüllermeier | 1 | 3423 | 213.52 |
| Johannes Fürnkranz | 2 | 2476 | 222.90 |
| Eneldo Loza Menc ´ ia | 3 | 488 | 25.84 |