Unique classifier selection approach for bagging algorithm

Bagging is a popular method that improves the classification accuracy for any learning algorithm. A trial and error classifier feeding with the Bagging algorithm is a regular practice for classification tasks in the machine learning community. In this research we propose a rule based method using statistical information for unique classifier selection. The generated rules are verified using 113 classification problems with cross validation approach. That makes Bagging is a computationally faster algorithm and provides a unique solution for classifier selection.