a collection of Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs).
HEMDAG library: * implements several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs); * reconciles flat predictions with the topology of the ontology; * can enhance predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; * provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; * is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but can be safely applied to tree-structured taxonomies as well (e.g. FunCat), since trees are DAGs; * scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; * provides several utility functions to process and analyze graphs; * provides several performance metrics to evaluate HEMs algorithms.
