Earticle

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

키워드

저자

• 변해원 [ Haewon Byeon | Department of AI-Software, Inje University, South Korea. ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620