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Blocking Reduction Strategies in Hierarchical Text Classification
 

Summary: Blocking Reduction Strategies in
Hierarchical Text Classification
Aixin Sun, Ee-Peng Lim, Senior Member, IEEE,
Wee-Keong Ng, Member,
IEEE Computer Society, and
Jaideep Srivastava, Fellow, IEEE
Abstract--One common approach in hierarchical text classification involves
associating classifiers with nodes in the category tree and classifying text
documents in a top-down manner. Classification methods using this top-down
approach can scale well and cope with changes to the category trees. However, all
these methods suffer from blocking which refers to documents wrongly rejected by
the classifiers at higher-levels and cannot be passed to the classifiers at lower-
levels. In this paper, we propose a classifier-centric performance measure known
as blocking factor to determine the extent of the blocking. Three methods are
proposed to address the blocking problem, namely, Threshold Reduction,
Restricted Voting, and Extended Multiplicative. Our experiments using Support
Vector Machine (SVM) classifiers on the Reuters collection have shown that they
all could reduce blocking and improve the classification accuracy. Our
experiments have also shown that the Restricted Voting method delivered the best
performance.

  

Source: Aixin, Sun - School of Computer Engineering, Nanyang Technological University

 

Collections: Computer Technologies and Information Sciences