Semi-supervised Predictive Clustering Trees for Improved Multi-label and Hierarchical Multi-label Classification

The core message of this paper is that semi-supervised predictive clustering trees (SSL-PCTs) and their ensemble versions (SSL-RFs) can significantly improve the predictive performance of multi-label classification (MLC) and hierarchical multi-label classification (HMLC) tasks compared to their supervised counterparts, by leveraging both labeled and unlabeled data.