Roughly Balanced Bagging for Imbalanced Data

Imbalanced class problems appear in many real applications of classification learning. We propose a novel sampling method to improve bagging for data sets with skewed class distributions. In our new sampling method “Roughly Balanced Bagging” (RB Bagging), the number of samples in the largest and smallest classes are different, but they are effectively balanced when averaged over all subsets, which supports the approach of bagging in a more appropriate way. Our method is different from the existing bagging methods for imbalanced data which draw exactly the same numbers of majority and minority examples for the sampled subset data. In addition, our method makes full use of all of the minority examples by under-sampling, which is efficiently done by using negative binomial distributions. RB Bagging outperforms the existing “balanced” methods and other common methods, as shown by the experiments using benchmark and real-world data sets.