Applying TabTransformer and Conditional GAN Algorithms for Enhancing Job Satisfaction Prediction of South Korean College Graduates
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초록
This study integrates TabTransformer and CTGAN for predicting job satisfaction among South Korean college graduates. TabTransformer handles complex tabular data relationships with self-attention, while CTGAN generates high-quality synthetic samples. The combined approach achieves an accuracy of 0.85, precision of 0.83, recall of 0.82, F1-score of 0.82, and an AUC of 0.88. Cross-validation confirms the model's robustness and generalizability with a mean accuracy of 0.85 and a standard deviation of 0.008. The integration of TabTransformer and CTGAN enhances predictive accuracy and model generalizability, providing valuable insights for employment policy and research.
Abstract
This study integrates TabTransformer and CTGAN for predicting job satisfaction among South Korean college graduates. TabTransformer handles complex tabular data relationships with self-attention, while CTGAN generates high-quality synthetic samples. The combined approach achieves an accuracy of 0.85, precision of 0.83, recall of 0.82, F1-score of 0.82, and an AUC of 0.88. Cross-validation confirms the model's robustness and generalizability with a mean accuracy of 0.85 and a standard deviation of 0.008. The integration of TabTransformer and CTGAN enhances predictive accuracy and model generalizability, providing valuable insights for employment policy and research.
참고문헌 (38)
- Locke, E. A., "Handbook of Industrial and Organizational Psychology" (1976)
- Spector, P. E., "Job Satisfaction: Application, Assessment, Causes, and Consequences" (1997), Sage Publications
- Smith, M. J., "New technology, automation, and work organization: Stress problems and improved technology implementation strategies", International Journal of Human Factors in Manufacturing, 5(1), pp.99~116 (1995)
- LeCun, Y., "Deep learning", Nature, 521(7553), pp.436~444 (2015)
- Breiman, L., "Random Forests", Machine Learning, 45(1), pp.5~32 (2001)
- Cortes, C., "Support-Vector Networks", Machine Learning, 20(3), pp.273~297 (1995)
- Agresti, A., "Categorical Data Analysis" (2020), Wiley-Interscience
- Chawla, N. V., "SMOTE: Synthetic Minority Over-sampling Technique", Journal of Artificial Intelligence Research, 16, pp.321~357 (2002)
- Goodfellow, I., "Deep Learning" (2016), MIT Press
- Bello, I., "Attention Augmented Convolutional Networks", Proceedings of the IEEE, 2019, 3286~3295
- Creswell, A., "Generative Adversarial Networks: An Overview", IEEE Signal Processing Magazine, 35(1), pp.53~65 (2018)
- Goodfellow, I., "Generative Adversarial Nets", Advances in Neural Information Processing Systems, 27, pp.2672~2680 (2014)
- Xu, L., "Modeling Tabular data using Conditional GAN", Advances in Neural Information Processing Systems, 32, pp.7333~7343 (2019)
- Karras, T., "A Style-Based Generator Architecture for Generative Adversarial Networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4401~4410 (2019)
- Cutler, D. R., "Random forests for classification in ecology", Ecology, 88(11), pp.2783~2792 (2007)
- Quinlan, J. R., "Induction of Decision Trees", Machine Learning, 1(1), pp.81~106 (1986)
- Cortes, C., "Support-Vector Networks", Machine Learning, 20(3), pp.273~297 (1995)
- Friedman, J. H., "Greedy Function Approximation:A Gradient Boosting Machine", The Annals of Statistics, 29(5), pp.1189~1232 (2001)
- Vaswani, A., "Attention is All You Need", Advances in Neural Information Processing Systems, 30, pp.5998~6008 (2017)
- Arik, S. O., "TabNet: Attentive Interpretable Tabular Learning", Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), pp.6679~6687 (2021)
- Creswell, A., "Generative Adversarial Networks: An Overview", IEEE Signal Processing Magazine, 35(1), pp.53~65 (2018)
- Johnson, A. M., "Generative modeling for maximizing precision and recall in data deficiency and imbalance", Proceedings of the Workshop on Health Intelligence, W3C Workshop (2017)
- Wang, Y., "Generalizing from a few examples: A survey on few-shot learning", ACM Computing Surveys (CSUR), 53(3), pp.1~34 (2019)
- Nguyen, H. V., "LIME-based ensemble machine for predicting performance status of patients with liver cancer", Digital Health, 9 (2023)
- Smith, J., "Enhancing Predictive Analytics with Generative Models", Journal of Data Science and Analytics, 7(2), pp.89~103 (2020)
- Lee, J., "Integrating Generative Adversarial Networks and Transformer Models for Improved Classification in NLP", Artificial Intelligence Review, 54(4), pp.2903~2923 (2021)
- Chung, T., "CTGAN: Conditional Tabular Generative Adversarial Network for Data Augmentation", Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp.2025~2028 (2019)
- Zhou, P., "TabTransformer:Increasing Prediction Accuracy of Tabular Data Using Transformer Architecture", Proceedings of the 36th International Conference on Machine Learning, 139, pp.9123~9132 (2020)
- Liu, H., "A generative adversarial approach for feature learning from imbalanced data", Journal of Machine Learning Research, 18(1), pp.7934~7967 (2018)
- Radford, A., "Improving Language Understanding by Generative Pre-Training" (2018), OpenAI
- Karras, T., "A Style-Based Generator Architecture for Generative Adversarial Networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)