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Computer and Information Science, ACIS International Conference on (2007)
Melbourne, Australia
July 11, 2007 to July 13, 2007
ISBN: 0-7695-2841-4
pp: 532-536
A. B. M Shawkat Ali , Central Queensland University, Australia
Tony Dobele , Central Queensland University, Australia
ABSTRACT
Boosting is a general approach for improving classifier performances. In this research we investigated these issues with the latest Boosting algorithm AdaBoostM1. A trial and error classifier feeding with the AdaBoostM1 algorithm is a regular practice for classification tasks in the research community. We provide a novel statistical information-based rule method for unique classifier selection with the AdaBoostM1 algorithm. The solution also verified a wide range of benchmark classification problems.
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CITATION

A. B. Ali and T. Dobele, "A Novel Classifier Selection Approach for Adaptive Boosting Algorithms," 2007 International Conference on Computer and Information Science(ICIS), Melbourne, Qld., 2007, pp. 532-536.
doi:10.1109/ICIS.2007.38
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