  Imbalanced response variable distribution is a common occurrence in data
science. In fields such as fraud detection, medical diagnostics, system
intrusion detection and many others where abnormal behavior is rarely observed
the data under study often features disproportionate target class distribution.
One common way to combat class imbalance is through resampling the minority
class to achieve a more balanced distribution. In this paper, we investigate
the performance of the sampling method based on kernel density estimation
(KDE). We believe that KDE offers a more natural way of generating new
instances of minority class that is less prone to overfitting than other
standard sampling techniques. It is based on a well established theory of
nonparametric statistical estimation. Numerical experiments show that KDE can
outperform other sampling techniques on a range of real life datasets as
measured by F1-score and G-mean. The results remain consistent across a number
of classification algorithms used in the experiments. Furthermore, the proposed
method outperforms the benchmark methods irregardless of the class distribution
ratio. We conclude, based on the solid theoretical foundation and strong
experimental results, that the proposed method would be a valuable tool in
problems involving imbalanced class distribution.
