Hierarchical Text Classification with Reinforced Label Assignment
Table 4: Performance comparison on Functional Catalogue and Gene Ontology. We compare with state-of-the-art hierarchical classification methods that take exactly the same raw features as input (i.e., we exclude models designed specifically for text objects).
['[BOLD] Method', 'Micro-F1', '[BOLD] FunCat Macro-F1', 'EBF', 'Micro-F1', '[BOLD] GO Macro-F1', 'EBF']
3. Results on Functional Genomics Prediction. An SVM classifier is also evaluated to better understand the difficulties of the task. Note that the metric area under the average precision-recall curve (AUPRC) Wehrmann et al. As one can see, HiLAP outperforms all the baselines on both datasets by a large margin. In particular, we observe significant improvement on Macro-F1 over the best baseline (47.9% and 53.9%, respectively), which shows that our method is especially better at classifying sparse labels than previous approaches.