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

Abstract

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 ageingrelated 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 HDNnHPC 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.

New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins

“…This work uses the HDN-NSR variation of the HDN algorithm 100 (Fabris and Freitas, 2015), which uses a more sophisticated procedure to find the relationships between the classes of the hierarchy. It has been shown that HDN-NSR has overall better predictive performance than HDN and greater potential for finding relations that were not initially present in the class hierarchy, possibly useful for 105 biologists studying the ageing process.…”