Gamma distribution-based sampling for imbalanced data
This addresses bias in classification algorithms for fields like medical diagnostics and fraud detection, but it is incremental as it builds on existing resampling techniques.
The paper tackles the problem of imbalanced class distribution in classification by proposing a novel resampling method based on a gamma distribution to generate new minority instances, resulting in outperforming state-of-the-art methods by achieving the best results on 12 out of 24 datasets compared to SMOTE's 1.
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.