A Novel Extended Hierarchical Dependence Network Method Based on Non-hierarchical Predictive Classes and Applications to Ageing-Related Data

We propose a novel algorithm for hierarchical classification, the Hierarchical Dependence Network based on non-Hierarchical Predictive Classes (HDN-nHPC) algorithm. HDN-nHPC uses relationships among predictive classes that are not descendants or ancestors of each other to improve classification performance and, at the same time, provide insights to non-obvious predictive class relationships. To test our algorithm and baselines, we have used hierarchical ageing-related datasets where the classes are terms in the Gene Ontology. We have concluded, based on our experiments, that using non-hierarchical predictive class relationships improves the performance of the classification algorithm and that, considering one out of three accuracy measures, the HDN-nHPC is statistically significantly better than the other three algorithms that we have tested, while no statistical significant differences were found on the other two measures.