Research Article

DOI :10.26650/acin.1486319 IUP :10.26650/acin.1486319 Full Text (PDF)

DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction

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

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APA

Tokmak, M. (2019). DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction. Acta Infologica, 0(0), -. https://doi.org/10.26650/acin.1486319

AMA

Tokmak M. DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction. Acta Infologica. 2019;0(0):-. https://doi.org/10.26650/acin.1486319

ABNT

Tokmak, M. DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction. Acta Infologica, [Publisher Location], v. 0, n. 0, p. -, 2019.

Chicago: Author-Date Style

Tokmak, Mahmut,. 2019. “DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction.” Acta Infologica 0, no. 0: -. https://doi.org/10.26650/acin.1486319

Chicago: Humanities Style

Tokmak, Mahmut,. “DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction.” Acta Infologica 0, no. 0 (Mar. 2025): -. https://doi.org/10.26650/acin.1486319

Harvard: Australian Style

Tokmak, M 2019, 'DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction', Acta Infologica, vol. 0, no. 0, pp. -, viewed 10 Mar. 2025, https://doi.org/10.26650/acin.1486319

Harvard: Author-Date Style

Tokmak, M. (2019) ‘DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction’, Acta Infologica, 0(0), pp. -. https://doi.org/10.26650/acin.1486319 (10 Mar. 2025).

MLA

Tokmak, Mahmut,. “DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction.” Acta Infologica, vol. 0, no. 0, 2019, pp. -. [Database Container], https://doi.org/10.26650/acin.1486319

Vancouver

Tokmak M. DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction. Acta Infologica [Internet]. 10 Mar. 2025 [cited 10 Mar. 2025];0(0):-. Available from: https://doi.org/10.26650/acin.1486319 doi: 10.26650/acin.1486319

ISNAD

Tokmak, Mahmut. “DFCEA: Deep Forest Classifier-Based Employee Attrition Prediction”. Acta Infologica 0/0 (Mar. 2025): -. https://doi.org/10.26650/acin.1486319

TIMELINE

Submitted	18.05.2024
Accepted	31.12.2024
Published Online	03.02.2025

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