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Published Articles >> Table of Contents >> Abstract
Sixth IEEE International Conference on Data Mining (ICDM'06)
pp. 33-42
Learning to Use a Learned Model: A Two-Stage Approach to Classification
Maria-Luiza Antonie, University of Alberta, Canada
Osmar R. Zaiane, University of Alberta, Canada
Robert C. Holte, University of Alberta, Canada
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.97
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| Abstract |
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Association rule-based classifiers have recently emerged
as competitive classification systems. However, there are
still deficiencies that hinder their performance. One defi-
ciency is the use of rules in the classification stage. Current
systems assign classes to new objects based on the
best rule applied or on some predefined scoring of multiple
rules. In this paper we propose a new technique where
the system automatically learns how to use the rules. We
achieve this by developing a two-stage classification model.
First, we use association rule mining to discover classification
rules. Second, we employ another learning algorithm
to learn how to use these rules in the prediction process.
Our two-stage approach outperforms C4.5 and RIPPER on
the UCI datasets in our study, and outperforms other rule-learning
methods on more than half the datasets. The versatility
of our method is also demonstrated by applying it
to text classification, where it equals the performance of the
best known systems for this task, SVMs.
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 Additional Information
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Citation:
Maria-Luiza Antonie, Osmar R. Zaiane, Robert C. Holte,
"Learning to Use a Learned Model: A Two-Stage Approach to Classification,"
icdm,
pp. 33-42,
Sixth IEEE International Conference on Data Mining (ICDM'06),
2006
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