Uncertainty instructed multi-granularity decision for large-scale hierarchical classification

Abstract

Hierarchical classification identifies a sample from the root node to a leaf node along the hierarchical structures of labels. It is often difficult to perform leaf-node prediction owing to ambiguous or incomplete information In such scenarios, a multi-granularity decision needs to be designed to stop the sample at a coarse-grained node rather than going rashly to a wrong leaf node. Conventionally, the probabilities of assigning the sample to its child nodes are regarded as evidence to decide whether to let the sample stop or go. However, the fact that the output of an unreliable classifier cannot be used to appropriately form the basis of decision making, especially with the existence of data uncertainties, is overlooked by existing methods. Therefore, we model the multi-granularity decision problem from an uncertainty perspective and consider that the uncertainties of multi-granularity decision emanate from the prediction of the classifier and the intrinsic information of the data. Inspired by the theory of fuzzy rough sets, a new measure is proposed to describe the intrinsic uncertainty in data. Integrating with the prediction uncertainty of the classifier, the hierarchical structure is used to design an effective optimization method that ensures proper multi-granularity decisions. Experiments show that the proposed algorithm achieves state-of-the-art performance.