Fig. 2
From: A top-down supervised learning approach to hierarchical multi-label classification in networks
Framework of the hierarchical multi-label classification approach. The approach is split into three stages: data pre-processing, class prediction and performance evaluation. The approach is applied for every resulting sub-hierarchy \(\mathsf {H}'\) independently. Ancestral relations between classes are included in the model as features with the prediction of ancestors and are represented by the upward arrow in the prediction stage. In addition, a correction mechanism for inconsistencies is included by means of cumulative probabilities, which are computed according to the path of classes in the sub-hierarchy. If the probability of association between a node and a class is close to zero, then the cumulative probability of the association between the same node and the descendant classes will be close to zero as well