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Towards Using Reranking in Hierarchical Classification

Proceedings of the Joint ECML/PKDD-PASCAL Workshop on Large-Scale Hierarchical Classification; September 5, 2011; Athens, Greece
Year of publication: 
Conference paper - peer reviewed

We consider the use of reranking as a way to relax typical in-
dependence assumptions often made in hierarchical multilabel classification.
Our reranker is based on (i) an algorithm that generates promising k-best
classification hypotheses from the output of local binary classifiers that clas-
sify nodes of a target tree-shaped hierarchy; and (ii) a tree kernel-based
reranker applied to the classification tree associated with the hypotheses
above. We carried out a number of experiments with this model on the
Reuters corpus: we firstly show the potential of our algorithm by computing
the oracle classification accuracy. This demonstrates that there is a signifi-
cant room for potential improvement of the hierarchical classifier. Then, we
measured the accuracy achieved by the reranker, which shows a significant
performance improvement over the baseline.

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