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 language||English (US)|
|Number of pages||4|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|State||Published - Oct 2004|
Bibliographical noteFunding Information:
The work is partially supported by the SingAREN21 research grant M48020004, Singapore.
- Data mining
- Text mining