A novel classifier selection approach for adaptive boosting algorithms
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.