Blocking reduction strategies in hierarchical text classification

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 t...

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Main Authors: LIM, Ee Peng, SUN, Aixin, NG, Wee-Keong, SRIVASTAVA, Jaideep
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Language:English
Published: Institutional Knowledge at Singapore Management University 2004
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Online Access:https://ink.library.smu.edu.sg/sis_research/124
https://ink.library.smu.edu.sg/context/sis_research/article/1123/viewcontent/Blocking_reduction_strategies_in_hierarchical_text_classification.pdf
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spelling sg-smu-ink.sis_research-11232018-06-29T02:09:14Z Blocking reduction strategies in hierarchical text classification LIM, Ee Peng SUN, Aixin NG, Wee-Keong SRIVASTAVA, Jaideep 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. 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. 2004-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/124 info:doi/10.1109/TKDE.2004.50 https://ink.library.smu.edu.sg/context/sis_research/article/1123/viewcontent/Blocking_reduction_strategies_in_hierarchical_text_classification.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data mining text mining classification Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data mining
text mining
classification
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Data mining
text mining
classification
Databases and Information Systems
Numerical Analysis and Scientific Computing
LIM, Ee Peng
SUN, Aixin
NG, Wee-Keong
SRIVASTAVA, Jaideep
Blocking reduction strategies in hierarchical text classification
description 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. 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.
format text
author LIM, Ee Peng
SUN, Aixin
NG, Wee-Keong
SRIVASTAVA, Jaideep
author_facet LIM, Ee Peng
SUN, Aixin
NG, Wee-Keong
SRIVASTAVA, Jaideep
author_sort LIM, Ee Peng
title Blocking reduction strategies in hierarchical text classification
title_short Blocking reduction strategies in hierarchical text classification
title_full Blocking reduction strategies in hierarchical text classification
title_fullStr Blocking reduction strategies in hierarchical text classification
title_full_unstemmed Blocking reduction strategies in hierarchical text classification
title_sort blocking reduction strategies in hierarchical text classification
publisher Institutional Knowledge at Singapore Management University
publishDate 2004
url https://ink.library.smu.edu.sg/sis_research/124
https://ink.library.smu.edu.sg/context/sis_research/article/1123/viewcontent/Blocking_reduction_strategies_in_hierarchical_text_classification.pdf
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