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Abstract
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Article Information:
An Empirical Study of Combining Boosting-BAN and Boosting-MultiTAN
Xiaowei Sun and Hongbo Zhou
Corresponding Author: Xiaowei Sun
Submitted: September 24, 2012
Accepted: November 12, 2012
Published: May 30, 2013 |
Abstract:
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An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Boosting-BAN classifier is considered stronger than Boosting-Multi TAN on noise-free data. However, there are strong empirical indications that Boosting-MultiTAN is much more robust than Boosting-BAN in noisy settings. For this reason, in this study we built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN and Boosting-MultiTAN ensembles with 25 sub-classifiers on standard benchmark datasets and the proposed technique was the most accurate.
Key words: Bayesian network classifier, combination method, data mining, boosting , , ,
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Cite this Reference:
Xiaowei Sun and Hongbo Zhou, . An Empirical Study of Combining Boosting-BAN and Boosting-MultiTAN. Research Journal of Applied Sciences, Engineering and Technology, (24): 5550-5555.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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