A Meta-Top-Down Method for Large-Scale Hierarchical Classification

作者：Wang, Xiao Lin^*; Zhao, Hai; Lu, Bao Liang

来源：IEEE Transactions on Knowledge and Data Engineering, 2014, 26(3): 500-513.

DOI：10.1109/TKDE.2013.30

• Large-scale hierarchical classification
• metalearning
• ensemble learning
• metaclassification
• top-down method
• text classification

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.

• 出版日期2014-3
• 单位Microsoft; 上海交通大学