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A GAN-based hybrid approach for addressing class imbalance in machine learning

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Observational analysis reveals improved performance in class imbalance using GAN and ensemble techniques.

The proposed GAN-based hybrid approach significantly improves prediction performance on imbalanced datasets.
Results indicate that the new technique outperforms traditional methods like SMOTEENN and SMOTETomek.
Hybrid models combining oversampling, undersampling, and ensemble techniques reduce model overfitting effectively.
Evaluation on two datasets shows clear advantages in handling class imbalance challenges.

Kim et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

Observational analysis reveals improved performance in class imbalance using GAN and ensemble techniques.

The proposed GAN-based hybrid approach significantly improves prediction performance on imbalanced datasets.
Results indicate that the new technique outperforms traditional methods like SMOTEENN and SMOTETomek.
Hybrid models combining oversampling, undersampling, and ensemble techniques reduce model overfitting effectively.
Evaluation on two datasets shows clear advantages in handling class imbalance challenges.

Kim et al. (2025) studied this question.

View Full Paper

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