Галерея 3501547

Галерея 3501547
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Research on the Prediction of Employee Turnover Behavior and Its Interpretability
Published: 31 December 2021 Publication History
EITCE 2021: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering
Qualifiers research-article Research Refereed limited
EITCE 2021
Paper Acceptance Rate 294 of 531 submissions, 55% Overall Acceptance Rate 508 of 972 submissions, 52%
Alao, D. and A.B. Adeyemo, ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS. Computing Information Systems Development Informatics & Allied Research Journal, 2014. 4(1). Google Scholar Sikaroudi, E., et al., A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing). Journal of industrial and systems engineering, 2015. 8(4): p. 106--121. Google Scholar Sisodia, D.S., S. Vishwakarma, and A. Pujahari. Evaluation of machine learning models for employee churn prediction. in 2017 International Conference on Inventive Computing and Informatics (ICICI). 2017. IEEE. Google Scholar Cross Ref Zhang, H., et al. Analysis and prediction of employee turnover characteristics based on machine learning. in 2018 18th International Symposium on Communications and Information Technologies (ISCIT). 2018. IEEE. Google Scholar Cross Ref Jain, N., A. Tomar, and P.K. Jana, A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning. Journal of Intelligent Information Systems, 2021. 56(2): p. 279--302. Google Scholar Chen, T., et al., Xgboost: extreme gradient boosting. R package version 0.4-2, 2015. 1(4): p. 1--4. Google Scholar Chen, T. and C. Guestrin. Xgboost: A scalable tree boosting system. in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016. Google Scholar Digital Library Fernández, A., et al., SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. Journal of artificial intelligence research, 2018. 61: p. 863--905. Google Scholar Cross Ref Kong Xiangwei, Tang Xinze, and Wang Ziming, A review of research on the interpretability of artificial intelligence decision-making. System engineering theory and practice. 41(2): p. 524--536. Google Scholar Ribeiro, M.T., S. Singh, and C. Guestrin. "Why should i trust you?" Explaining the predictions of any classifier. in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016. Google Scholar Digital Library Shapley, L.S., 17. A value for n-person games. 2016: Princeton University Press. Google Scholar Lundberg, S.M., et al., From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2020. 2(1): p. 56--67. Google Scholar Lundberg, S.M. and S.-I. Lee. A unified approach to interpreting model predictions. in Proceedings of the 31st international conference on neural information processing systems. 2017. Google Scholar Digital Library Lundberg, S.M., G.G. Erion, and S.-I. Lee, Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888, 2018. Google Scholar Sundararajan, M. and A. Najmi. The many Shapley values for model explanation. in International Conference on Machine Learning. 2020. PMLR. Google Scholar Janzing, D., L. Minorics, and P. Blöbaum. Feature relevance quantification in explainable AI: A causal problem. in International Conference on Artificial Intelligence and Statistics. 2020. PMLR. Google Scholar Khalid, S., T. Khalil, and S. Nasreen. A survey of feature selection and feature extraction techniques in machine learning. in 2014 science and information conference. 2014. IEEE. Google Scholar Kira, K. and L.A. Rendell. The feature selection problem: Traditional methods and a new algorithm. in Aaai. 1992. Google Scholar
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Employee turnover is a key issue that companies must consider, and predicting employee turnover behavior can help companies reduce losses. However, the current Mechanism-Driven prediction method has some problems such as simplified assumptions and low accuracy. Scholars have begun to use it the Data-Driven method establishes an employee turnover behavior prediction model, but there is a lack of interpretability research on the model, which cannot effectively explain the prediction results, it will hinder the actual implementation of the method. This research establishes the RSGSBoost-SHAP prediction and interpretation framework on the basis of combing relevant literature. The framework can directly predict employee turnover behavior based on input characteristics, and explore the mechanism of the characteristics of the turnover behavior, providing management inspiration for enterprises. In the prediction results, AUC is 0.9224, and Recall is 0.8487, reaching the expected accuracy. The interpretation results elaborated on the mechanism of employee characteristics affecting their turnover behavior.
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