Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy

Abstract

Hierarchical multi-label classification aims at learning a multi-label classifier from a dataset whose labels are organized into a hierarchical structure. To the best of our knowledge, we propose for the first time the problem of finding a multi-label classifier given a partially labeled hierarchical multi-label dataset. We also assume the situation where the classifier cannot access hierarchical information during training. This work proposes an iterative framework for learning both multi-labels and a hierarchical structure of classes. When training a multi-label classifier from partial labels, our model extracts a class hierarchy from the classifier output using our hierarchy extraction algorithm. Then, our proposed loss exploits the extracted hierarchy to train the classifier. Theoretically, we show that our hierarchy extraction algorithm correctly finds the unknown hierarchy under a mild condition, and we prove that our loss function of multi-label classification with such hierarchy becomes an unbiased estimator of true multi-label classification risk. Our experiments show that our model obtains a class hierarchy close to the ground-truth dataset hierarchy, and simultaneously, our method outperforms previous methods for hierarchical multi-label classification and multi-label classification from partial labels.