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Published Articles >> Table of Contents >> Abstract
Sixth IEEE International Conference on Data Mining (ICDM'06)
pp. 869-873
A Balanced Ensemble Approach to Weighting Classifiers for Text Classification
Gabriel Pui Cheong Fung, The Chinese University of Hong Kong, Hong Kong
Jeffrey Xu Yu, The Chinese University of Hong Kong, Hong Kong
Haixun Wang, IBM T.J. Watson Research Center, USA
David W. Cheung, The University of Hong Kong, Hong Kong
Huan Liu, Arizona State University, USA
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.2
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| Abstract |
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This paper studies the problem of constructing an effective
heterogeneous ensemble classifier for text classification.
One major challenge of this problem is to formulate
a good combination function, which combines the decisions
of the individual classifiers in the ensemble. We
show that the classification performance is affected by three
weight components and they should be included in deriving
an effective combination function. They are: (1) Global
effectiveness, which measures the effectiveness of a member
classifier in classifying a set of unseen documents; (2)
Local effectiveness, which measures the effectiveness of a
member classifier in classifying the particular domain of
an unseen document; and (3) Decision confidence, which
describes how confident a classifier is when making a decision
when classifying a specific unseen document. We propose
a new balanced combination function, called Dynamic
Classifier Weighting (DCW), that incorporates the afore-mentioned
three components. The empirical study demonstrates
that the new combination function is highly effective
for text classification.
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 Additional Information
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Citation:
Gabriel Pui Cheong Fung, Jeffrey Xu Yu, Haixun Wang, David W. Cheung, Huan Liu,
"A Balanced Ensemble Approach to Weighting Classifiers for Text Classification,"
icdm,
pp. 869-873,
Sixth IEEE International Conference on Data Mining (ICDM'06),
2006
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