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Issue No. 03 - March (2014 vol. 26)
ISSN: 1041-4347
pp: 500-513
Xiao-Lin Wang , Shanghai Jiao Tong University, Shanghai
Hai Zhao , Shanghai Jiao Tong University, Shanghai
Bao-Liang Lu , Shanghai Jiao Tong University, Shanghai
ABSTRACT
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.
INDEX TERMS
text classification, Large-scale hierarchical classification, metalearning, ensemble learning, metaclassification, top-down method,
CITATION
Xiao-Lin Wang, Hai Zhao, Bao-Liang Lu, "A Meta-Top-Down Method for Large-Scale Hierarchical Classification", IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. , pp. 500-513, March 2014, doi:10.1109/TKDE.2013.30
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