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A Novel Classifier Selection Approach for Adaptive Boosting Algorithms
Melbourne, Australia July 11-July 13
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICIS.2007.386th IEEE/ACIS International Conferenc ...
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A. B. M Shawkat Ali, Central Queensland University, Australia
Tony Dobele, Central Queensland University, Australia
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.
Citation:
A. B. M Shawkat Ali, Tony Dobele, "A Novel Classifier Selection Approach for Adaptive Boosting Algorithms," icis, pp.532-536, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 2007
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