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A Meta-Top-Down Method for Large-Scale Hierarchical Classification
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DOI：10.1109/TKDE.2013.30.png)
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Recent large-scale hierarchical classification tasks typically have tens of thousands of classes on which the most widely used approach to multiclass classification-one-versus-rest-becomes intractable due to computational complexity. The top-down methods are usually adopted instead, but they are less accurate because of the so-called error-propagation problem in their classifying phase. To address this problem, this paper proposes a meta-top-down method that employs metaclassification to enhance the normal top-down classifying procedure. The proposed method is first analyzed theoretically on complexity and accuracy, and then applied to five real-world large-scale data sets. The experimental results indicate that the classification accuracy is largely improved, while the increased time costs are smaller than most of the existing approaches.
Keyword:
Large-scale hierarchical classification
metalearning
ensemble learning
metaclassification
top-down method
text classification
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