Chained Path Evaluation for Hierarchical Multi-Label Classification

In this paper we propose a novel hierarchical multi-label clas- sification approach for tree and directed acyclic graph (DAG) hierarchies. The method predicts a single path (from the root to a leaf node) for tree hierarchies, and multiple paths for DAG hierarchies, by combining the predictions of every node in each possible path. In contrast with previous approaches, we evaluate all the paths, training local classifiers for each non-leaf node. The approach incorporates two contributions; (i) a cost is assigned to each node depending on the level it has in the hierarchy, giving more weight to correct predic- tions at the top levels; (ii) the relations between the nodes in the hierarchy are considered, by incorporating the parent label as in chained classifiers. The proposed approach was experimentally evaluated with 10 tree and 8 DAG hierarchi- cal datasets in the domain of protein function prediction. It was contrasted with various state-of-the-art hierarchical clas- sifiers using four common evaluation measures. The results show that our method is superior in almost all measures, and this difference is more significant in the case of DAG struc- tures.