Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification

Figure 7

The graph depicts the magnitude of improvement in the predictive performance over supervised PCTs enabled by (i) the variance function that considers both the descriptive and target spaces (-axis) and (ii) unlabeled data and the variance function that considers both the descriptive and target spaces (-axis). This is measured by the difference in the predictive performance of SL- and SL-PCT (; -axis) and of SSL-PCT and SL-PCT (; -axis). The positive values along the and axes denote that SL- or SSL-PCT improves over SL-PCTs, respectively. Clearly, the magnitude of improvement over SL-PCTs along the -axis is much larger than along the -axis, showing that the unlabeled examples are crucial for the performance of the SSL-PCTs. Each dot represents of one experiment (one dataset and one percentage of labeled data; all experiments are considered). (a) Multi-label classification. (b) Hierarchical multi-label classification.

(a)

(b)