Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía,
"Conformal Rule-Based Multi-label Classification"
, in Ute Schmid and Diedrich Wolter and Franziska Klügl: Proceedings of the 43d German Conference on Artificial Intelligence (KI-20), Serie Lecture Notes in Computer Science (LNCS), Vol. 12325, Springer, Bamberg, Germany, Seite(n) 290-296, 2020, ISBN: 978-3-030-58284-5
Original Titel:
Conformal Rule-Based Multi-label Classification
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 43d German Conference on Artificial Intelligence (KI-20)
Original Kurzfassung:
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.
Forschungs-