Sidra Jubair¹, Jie Yang^1,2,*, Bilal Ali³, Walid Emam⁴, Yusra Tashkandy⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 511-534, 2025, DOI:10.32604/cmes.2025.066514 - 31 July 2025

Abstract Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning, particularly when class overlap significantly deteriorates classification performance. Traditional oversampling methods often generate synthetic samples without considering density variations, leading to redundant or misleading instances that exacerbate class overlap in high-density regions. To address these limitations, we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE, a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap. The originality of WGAN-VDE lies in its density-aware sample refinement, ensuring that synthetic samples are positioned in underrepresented More >

Congcong Ma^1,2, Jiaqi Mi¹, Wanlin Gao^1,2, Sha Tao^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4243-4261, 2024, DOI:10.32604/cmc.2024.054506 - 12 September 2024

Abstract Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes. This task is prevalent in practical scenarios such as industrial fault diagnosis, network intrusion detection, cancer detection, etc. In imbalanced classification tasks, the focus is typically on achieving high recognition accuracy for the minority class. However, due to the challenges presented by imbalanced multi-class datasets, such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries, existing methods often do not perform well in multi-class imbalanced data… More >

Yalong Xie¹, Aiping Li^1,*, Biyin Hu², Liqun Gao¹, Hongkui Tu¹

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2707-2726, 2023, DOI:10.32604/cmc.2023.037039 - 08 October 2023

Abstract Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature… More >

M. Sandeep Kumar¹, Mohammad Zubair Khan^2,*, Sukumar Rajendran¹, Ayman Noor³, A. Stephen Dass¹, J. Prabhu¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4397-4409, 2022, DOI:10.32604/cmc.2022.025865 - 21 April 2022

Abstract Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar. The risk of diabetics can be lowered if the diabetic is found at the early stage. In recent days, several machine learning models were developed to predict the diabetic presence at an early stage. In this paper, we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics. The proposed method uses both the concept of over-sampling and More >