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Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Hussein Ibrahim Hussein1, Said Amirul Anwar2,*, Muhammad Imran Ahmad2

1 Department of Computer Techniques Engineering, AlSafwa University College, Karbala, 56001, Iraq
2 Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Arau, 02600, Perlis, Malaysia

* Corresponding Author: Said Amirul Anwar. Email:

Computers, Materials & Continua 2023, 75(1), 547-564.


Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed. In this technique, the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data. Next is the training of a balanced dataset using SVM. Since this algorithm requires an iterative process to search for the best penalty parameter during training, an improved SA algorithm was proposed for this task. In this proposed improvement, a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process. Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM. Registering at an average of 89.65% of accuracy for the binary class classification has demonstrated the good performance of the proposed works.


Cite This Article

H. I. Hussein, S. A. Anwar and M. I. Ahmad, "Imbalanced data classification using svm based on improved simulated annealing featuring synthetic data generation and reduction," Computers, Materials & Continua, vol. 75, no.1, pp. 547–564, 2023.

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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