A Hierarchical Ensemble Method for DAG-Structured Taxonomies

Structured taxonomies characterize several real world prob- lems, ranging from text categorization, to video annotation and protein function prediction. In this context “flat” learning methods may intro- duce inconsistent predictions, while structured output-aware learning methods can improve the accuracy of the predictions by exploiting the hierarchical relationships between classes. We propose a novel hierarchi- cal ensemble method able to provide theoretically guaranteed consistent predictions for any Directed Acyclic Graph (DAG)-structured taxonomy, and consequently also for any taxonomy structured according to a tree. Results with a complex real-world DAG-structured taxonomy involving about one thousand classes and twenty thousand of examples show that the proposed hierarchical ensemble approach significantly improves flat methods, especially in terms of precision/recall curves.