Research on the Prediction of Employee Turnover Behavior and Its Interpretability-Reference-Cited by-同舟云学术

Research on the Prediction of Employee Turnover Behavior and Its Interpretability

Published:2021-10-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering
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Author:

Tao Zhe¹,Wu CiSheng¹,Zhao ShuPing¹

Affiliation:

1. School of Management, Hefei University of Technology, Hefei Anhui China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3501409.3501547

Reference18 articles.

1. Alao , D. and A.B. Adeyemo , ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS . Computing Information Systems Development Informatics & Allied Research Journal , 201 4. 4(1). Alao, D. and A.B. Adeyemo, ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS. Computing Information Systems Development Informatics & Allied Research Journal, 2014. 4(1).

2. Sikaroudi , E. , et al. , A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing). Journal of industrial and systems engineering , 201 5. 8(4): p. 106 -- 121 . 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.

3. Evaluation of machine learning models for employee churn prediction

4. Analysis and Prediction of Employee Turnover Characteristics based on Machine Learning

5. 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 , 202 1. 56(2): p. 279 -- 302 . 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.

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