Bsmboost for imbalanced pattern classification problems

Numbers of samples in different classes are in nature imbalanced in many machine learning problems. Single classifier-based methods are subject to high variance. Therefore, ensemble-based methods are more suitable for dealing with imbalanced pattern classification problems. In this work, we propose a boosting-based method: BSMBoost which creates an ensemble of classifiers using samples selected by both the stochastic sensitivity measure (SSM) and the AdaBoost algorithm to yield higher and more robust performances. Experimental results show that the BSMBoost yields better and more robust performances in comparison to other state-of-the-art boosting-based imbalanced classification methods.