Abstract
The issue of imbalanced data in machine learning has gained significant attention in recent years. Imbalanced data, where one class has significantly fewer samples than others, can lead to poor performance for machine learning models, especially in detecting minority class samples. To address this problem, various resampling techniques have been proposed, including the popular SMOTE (Synthetic Minority Over-sampling TEchnique). However, SMOTE suffers from the overlapping problem and may misclassify samples near the separation boundaries. This paper presents a novel framework to optimise border-based-SMOTEs, including Borderline-SMOTE and SVM-SMOTE which were specifically developed to solve the problem of misclassifying border samples. The proposed method ensures that generated samples improve the decision boundaries and are free from overlapping issues. The proposed method is evaluated on synthetic and real-world datasets, and results demonstrate its effectiveness in enhancing the performance of machine learning models, particularly in classifying minority class samples.
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Acknowledgment
Tajul Miftahushudur would like to acknowledge the Scholarship provided by the Indonesian Endowment Fund for Education (LPDP). Halil Mertkan Sahin would like to acknowledge the Scholarship provided by the Ministry of National Education of the Republic of Türkiye.
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Miftahushudur, T., Sahin, H.M., Grieve, B., Yin, H. (2023). Enhanced SVM-SMOTE with Cluster Consistency for Imbalanced Data Classification. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_39
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