원문정보
초록
영어
This study investigates using Conditional Tabular Generative Adversarial Networks (CT-GAN) to generate synthetic data for turnover prediction in large employment datasets. The effectiveness of CT-GAN is compared with Adaptive Synthetic Sampling (ADASYN), Synthetic Minority Over-sampling Technique (SMOTE), and Random Oversampling (ROS) using Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forest (RF), and Extreme Learning Machines (ELM), evaluated with AUC and F1-scores. Results show that GAN-based techniques, especially CT-GAN, outperform traditional methods in addressing data imbalance, highlighting the need for advanced oversampling methods to improve classification accuracy in imbalanced datasets.
목차
Abstract
1. Introduction
2. Related works
3. Materials and Methods
3.1. Imbalance Ratio (IR)
3.2. Random Oversampling (ROS)
3.3. Synthetic Minority Over-Sampling Technique (SMOTE)
3.4. B-SMOTE
3.5. Adaptive Synthetic Sampling (ADASYN)
3.6. Conditional GAN (CGAN)
3.7. Conditional Tabular GAN (CT-GAN)
3.8. Modeling
3.9. Data source
3.10. Experimental design
3.11. Performance Evaluation Methods and Metrics
4. Results
5. Discussion
6. Conclusions
References
1. Introduction
2. Related works
3. Materials and Methods
3.1. Imbalance Ratio (IR)
3.2. Random Oversampling (ROS)
3.3. Synthetic Minority Over-Sampling Technique (SMOTE)
3.4. B-SMOTE
3.5. Adaptive Synthetic Sampling (ADASYN)
3.6. Conditional GAN (CGAN)
3.7. Conditional Tabular GAN (CT-GAN)
3.8. Modeling
3.9. Data source
3.10. Experimental design
3.11. Performance Evaluation Methods and Metrics
4. Results
5. Discussion
6. Conclusions
References
키워드
저자정보
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