Applying the self-training semi-supervised learning in hierarchical multi-label methods

Abstract

In classification problems with hierarchical structures of labels, the target function must assign several labels that are hierarchically organized. The hierarchical structures of labels can be used either for single-label (one label per instance) or multi-label classification problems (more than one label per instance). In general, classification tasks are usually trained using a standard supervised learning procedure. However, the majority of classification methods require a large number of training instances to be able to generalize the mapping function, making predictions with high accuracy. In order to smooth out this problem, the idea of semi-supervised learning has emerged. It combines labelled and unlabelled data during the training phase. Some semi-supervised methods have been proposed for single-label classification methods. However, very little effort has been done in the context of multi-label hierarchical classification. This paper proposes the use of a semi-supervised learning method for the multi-label hierarchical problems. In order to validate the feasibility of these methods, an empirical analysis will be conducted, comparing the proposed methods with their corresponding supervised versions. The main aim of this analysis is to observe whether the semi-supervised methods proposed in this paper have similar performance to the corresponding supervised versions.