DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction

Year 2025, Volume: 9 Issue: 1, 19 - 33, 30.06.2025

Mahmut Tokmak

https://doi.org/10.26650/acin.1486319

Abstract

For companies, employee attrition is an important issue because human resources are the most important resources of a company. In companies, employee attrition can have different causes. However, human resource managers must recognize employee attrition indicators in the early stages. Employee attrition can lead to organizational losses for various reasons, such as interruption of work, interruption of tasks that need to be performed, the cost of re-employment and retraining, and the risk of information leakage. Therefore, in this study, DFCEA: Deep Forest Classifier-Based Employee Attrition prediction model is proposed to predict employee attrition. Thus, this study aimed to help company managers take measures to prevent the loss of human resources. The IBM HR Analytics Employee Attrition & Performance dataset was used in this study. The dataset was subjected to data cleaning, data encoding, data normalization, and data balancing preprocessing. The model was then trained and tested using the Deep Forest algorithm. With the proposed method, 98.8% accuracy and 98.8% f1 score were obtained. The obtained performance metrics are compared with known machine learning methods and other studies, and the performance power of the proposed method is demonstrated. The results demonstrate that the proposed DFCEA framework is highly effective in predicting employee attrition. Therefore, the framework presented in this study can help researchers, organization leaders, and human resource professionals predict employee attrition and contribute to the development of new prediction models.

Keywords

Artificial Intelligence, Machine Learning, Deep Forest, Employee Attrition

References

• Al-Darraji, S., Honi, D. G., Fallucchi, F., Abdulsada, A., Giuliano, R., and Abdulmalik, H. A. (2021). EmploYee attrition prediction using deep neural networks. Computers. doi: 10.3390/computers10110141 google scholar
• Alharbi, H., Alshammari, O., Jerbi, H., Simos, T. E., Katsikis, V. N., Mourtas, S. D., & Sahas, R. D. (2023). A fresnel cosine integral WASD neural network for the classification of emploYee attrition. Mathematics, 11(6), 1506. google scholar
• AlshiddY, M. S., and Aljaber, B. N. (2023). EmploYee attrition prediction using nested ensemble learning techniques. International Journal of Advanced Computer Science and Applications, 14(7). google scholar
• Çavuşoğlu, Ü., and Kaçar, S. (2019). Anormal trafik tespiti için veri madenciliği algoritmalarının performans analizi. Academic Platform-Journal of Engineering and Science, 7(2), 205-216. google scholar
• Chaurasia, A., Kadam, S., Bhagat, K., Gauda, S., and Shingane, P. (2023). EmploYee attrition prediction using artificial neural networks. 2023 4th International Conference for Emerging Technology (INCET), 1-6. IEEE. google scholar
• Chung, D., Yun, J., Lee, J., & Jeon, Y. (2023). Predictive model of emploYee attrition based on stacking ensemble learning. Expert Systems with Applications, 215, 119364. google scholar
• da SiLva Mendes, R. F., & de Jesus, J. V. R. (2021). Exploraçao de modelos de aprendizado de mâguina e seleçâo de atributos para employee attrition. doi: 10.14210/cotb.v12.p267-272 google scholar
• FrYe, A., Boomhower, C., Smith, M., VitovskY, L., & Fabricant, S. (2018). EmpLoYee attrition: What makes an empLoYee quit? SMU Data Science Review, 1(1), 9. google scholar
• Fukui, S., Wu, W., GreenfieLd, J., SaLYers, M. P., Morse, G., Garabrant, J., Bass, E., KYere, E., DeLL, N. (2023). Machine Learning with human resources data: Predicting turnover among communitY mentaL heaLth center empLoYees. The Journal of Mental Health Policy and Economics, 26(2), 63-76. google scholar
• Guerranti, F., & Dimitri, G. M. (2022). A comparison of machine Learning approaches for predicting empLoYee attrition. Applied Sciences. doi: 10.3390/app13010267 google scholar
• Guo, Y., Liu, S., Li, Z., & Shang, X. (2018). Bcdforest: A boosting cascade deep forest modeL towards the cLassification of cancer subtYpes based on gene expression data. BMC Bioinformatics, 19(5), 1-13. google scholar
• Jain, P. K.; Jain, M.; PamuLa, R. (2020). ExpLaining and predicting empLoYees’ attrition: A machine Learning approach. Sn Applied Sciences. doi: 10.1007/s42452-020-2519-4 google scholar
• Jiang, X., Nazarpour, K., & Dai, C. (2023). ExpLainabLe and robust deep forests for EMG-Force modeLing. IEEE Journal of Biomedical and Health Informatics. google scholar
• KakuLapati, V., and Subhani, S. (2023). Predictive anaLYtics of empLoYee attrition using k-foLd methodoLogies. IJ Math. Sci. Comput., 1, 23-36. google scholar
• KamaLov, F., H.-H. Leung, and A. K. Cherukuri. (2023). Keep it simpLe: Random oversampLing for imbaLanced data. 2023 Advances in Science and Engineering Technology International Conferences (ASET), 1-4. IEEE. google scholar
• Mansor, N., Sani, N. F. M., & ALiff, M. (2021). Machine Learning for predicting empLoYee attrition. International Journal of Advanced Computer Science and Applications. doi: 10.14569/ijacsa.2021.0121149 google scholar
• Menardi, G., & ToreLLi, N. (2014). Training and assessing cLassification ruLes with imbaLanced data. Data Mining and Knowledge Discovery, 28, 92-122. google scholar
• Meraliyev, B., Karabayeva, A., Altynbekova, T., and Nematov, Y. (2023). Attrition rate measuring in human resource analytics using machine learning. 2023 17th International Conference on Electronics Computer and Computatlon (ICECCO), 1-6. IEEE. google scholar
• Metlek, S. (2021). Disease Detection from Cassava Leaf Images with Deep Learning Methods in Web Environment. International Journal of 3D Printing Technologies and Digital Industry, 5(3), 625-644. google scholar
• Qutub, A., Al-Mehmadi, A. R., Al-Hssan, M., Aljohani, R., and Alghamdi, H. M. (2021). Predict emploYee attrition using machine learning and ensemble methods. Int. J. Machine Learning Comput. doi: 10.18178/ijmlc.2021.11.2.1022 google scholar
• Raza, A., Munir, K. M., Almutairi, M., Younas, F., & Fareed, M. M. S. (2022). Predict emploYee attrition using machine learning approaches. Applied Sciences. doi: 10.3390/app12136424 google scholar
• Shaik, S. (2023). Machine learning-based emploYee attrition predicting. Asian Journal of Research in Computer Science. doi: 10.9734/ ajrcos/2023/v15i3323 google scholar
• Singh, D., & Singh, B. (2020). We investigate the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. google scholar
• Usha, P. M., and Balaji, N. V. (2021). A comparative studY of machine learning algorithms for emploYee attrition prediction. Iop Conference Series Materials Science and Engineering. doi: 10.1088/1757-899x/1085/1/012029 google scholar
• UstYannie, W., and Suprapto, S. (2020). Oversampling method to handling imbalanced datasets problem in binarY logistic regression algorithm. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(1), 1-10. google scholar
• Wardhani, F. H., and K. M. (2022). Predict emploYee attrition using logistic regression with feature selection. Sinkron. doi: 10.33395/ sinkron.v7i4.11783 google scholar
• Yao, L., Li, W., Zhang, Y., Deng, J., Pang, Y., Huang, Y., Chung, C., Yu, J., Chiang, Y., Lee, T.-Y. (2023). Accelerating the DiscoverY of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation. International Journal of Molecular Sciences, 24(5), 4328. doi: 10.3390/ijms24054328 google scholar
• Zhou, Z. H., & Feng, J. (2019). Deep forest. National Science Review, 6(1), 74-86. google scholar