  Imbalanced data occurs in a wide range of scenarios. The skewed distribution
of the target variable elicits bias in machine learning algorithms. One of the
popular methods to combat imbalanced data is to artificially balance the data
through resampling. In this paper, we compare the efficacy of a recently
proposed kernel density estimation (KDE) sampling technique in the context of
artificial neural networks. We benchmark the KDE sampling method against two
base sampling techniques and perform comparative experiments using 8 datasets
and 3 neural networks architectures. The results show that KDE sampling
produces the best performance on 6 out of 8 datasets. However, it must be used
with caution on image datasets. We conclude that KDE sampling is capable of
significantly improving the performance of neural networks.
