  Imbalanced class distribution is a common problem in a number of fields
including medical diagnostics, fraud detection, and others. It causes bias in
classification algorithms leading to poor performance on the minority class
data. In this paper, we propose a novel method for balancing the class
distribution in data through intelligent resampling of the minority class
instances. The proposed method is based on generating new minority instances in
the neighborhood of the existing minority points via a gamma distribution. Our
method offers a natural and coherent approach to balancing the data. We conduct
a comprehensive numerical analysis of the new sampling technique. The
experimental results show that the proposed method outperforms the existing
state-of-the-art methods for imbalanced data. Concretely, the new sampling
technique produces the best results on 12 out of 24 real life as well as
synthetic datasets. For comparison, the SMOTE method achieves the top score on
only 1 dataset. We conclude that the new technique offers a simple yet
effective sampling approach to balance data.
