Computer and Information Science, ACIS International Conference on (2007)
July 11, 2007 to July 13, 2007
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICIS.2007.38
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.
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.