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Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Masoud Muhammed Hassan1, Adel Sabry Eesa1,*, Ahmed Jameel Mohammed2, Wahab Kh. Arabo1

1 Department of Computer Science, University of Zakho, Duhok, 42001, Kurdistan Region, Iraq
2 Department of Information Technology, Duhok Polytechnic University, Duhok, 42001, Kurdistan Region, Iraq

* Corresponding Author: Adel Sabry Eesa. Email: email

Computers, Materials & Continua 2021, 69(1), 451-469.


Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this paper, we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering, called GK-Means. The new method aims to avoid generating noise and control imbalances between and within classes. Various experiments have been carried out with six classifiers and four oversampling methods. Experimental results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.


Cite This Article

M. Muhammed Hassan, A. Sabry Eesa, A. Jameel Mohammed and W. Kh. Arabo, "Oversampling method based on gaussian distribution and k-means clustering," Computers, Materials & Continua, vol. 69, no.1, pp. 451–469, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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