Labelling strategies for hierarchical multi-label classification techniques - Triguero, Isaac; Vens, Celine (Elsevier, 2016)
Many hierarchical multi-label classiﬁcation systems predict a real valued score for every (instance, class) couple, with a higher score reﬂecting more conﬁdence that the instance belongs to that class. These classiﬁers leave the conversion of these scores to an actual label set to the user, who applies a cut-oﬀ value to the scores. The predictive performance of these classiﬁers is usually evaluated using threshold independent measures like precision-recall curves. However, several applications require actual label sets, and thus an automatic labelling strategy.