Blocking reduction strategies in hierarchical text classification

Aixin Sun, Ee Peng Lim, Wee Keong Ng, Jaideep Srivastava

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1305-1308
Number of pages4
JournalIEEE Transactions on Knowledge and Data Engineering
Volume16
Issue number10
DOIs
StatePublished - Oct 2004

Bibliographical note

Funding Information:
The work is partially supported by the SingAREN21 research grant M48020004, Singapore.

Keywords

  • Classification
  • Data mining
  • Text mining

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