Bagging (Bootstrap Aggregating) has been proved to be a useful, effective and simple ensemble learning methodology. In generic bagging methods, all the classifiers which are trained on the different training datasets created by bootstrap resampling original datasets would be seen as base classifiers and their results would be combined to compute final result. This paper proposed a novel ensemble model that refines the bagging algorithm with an optimization process. The optimization process mainly emphasizes on how to select the optimal classifiers according to the accuracy and diversity of the base classifiers. While the select classifiers constitute the final base classifiers. The empirical results reveal that the new model does outperform the original method in terms of learning accuracy and complexity. © 2010 IEEE.