Kernel density estimation based sampling for imbalanced class distribution

Kamalov, Firuz
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Elsevier Inc.
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 of 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 to generate 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. © 2019 Elsevier Inc.
This article is not available at CUD collection. The version of scholarly record of this article is published in Information Sciences (2020), available online at:
Class imbalance, Imbalanced data, KDE, Kernel, Oversampling, Sampling, Diagnosis, Statistics, Classification algorithm, Kernel Density Estimation, Statistical estimation, Theoretical foundations, Intrusion detection
Kamalov, F. (2020). Kernel density estimation based sampling for imbalanced class distribution. Information Sciences, 512, 1192-1201.