Hybrid Loss for Hierarchical Multi–label Classification Network

Machine learning models for hierarchical multilabel classification (HMC) typically achieve low accuracy. This is because such models need not only predict multiple labels for each data instance, but also ensure that predicted labels conform to a given hierarchical structure. Existing state-of the-art strategies for HMC decouple the learning process from ensuring that predicted labels reside in a path of the hierarchy, thus inevitably degrading the overall classification accuracy.