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Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks
- Computer ScienceArXiv
- 2022
The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets, indicating that the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.
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