TY  - JOUR
ID  - ln:MlcAbstention
T1  - Reliable Multilabel Classification: Prediction with Partial Abstention
A1  - Nguyen, Vu-Linh
A1  - Hüllermeier, Eyke
JA  - Proceedings of the AAAI Conference on Artificial Intelligence
Y1  - 2020
VL  - 34
IS  - 04
SP  - 5264
EP  - 5271
UR  - https://ojs.aaai.org/index.php/AAAI/article/view/5972
M2  - doi: 10.1609/aaai.v34i04.5972
N2  - In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. We propose a formalization of MLC with abstention in terms of a generalized loss minimization problem and present first results for the case of the Hamming loss, rank loss, and F-measure, both theoretical and experimental.
ER  -