Probabilistic Cascading for Large Scale Hierarchical Classification
Table 1: Results for each approach per evaluation measure, using TF/IDF features. With bold we mark the best performing approach, given each evaluation measure.
['Evaluation Measure', 'Flat', 'Cascade', '[ITALIC] Ppath']
The main observation is that Ppath outperforms both Flat and Cascade. Another interesting result is, that Flat is the worst approach, according to Tree Induced Error. This is an indication that by ignoring the hierarchy (flat classification), the mistakes tend to be located further from the correct category in the hierarchy. This is very important in hierarchical classification, since different mistakes carry different weight. Misclassifying an instance to a sibling of the correct category is a smaller error than if it was classified to a category 5 nodes away. Flat evaluation measures, generally fail to capture this, so tree induced error, being the only hierarchical measure of the five that we use is more suitable for comparing the three approaches.