Issue No. 03 - March (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.30
Xiao-Lin Wang , Shanghai Jiao Tong University, Shanghai
Hai Zhao , Shanghai Jiao Tong University, Shanghai
Bao-Liang Lu , Shanghai Jiao Tong University, Shanghai
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.
text classification, Large-scale hierarchical classification, metalearning, ensemble learning, metaclassification, top-down method,
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