Proceedings of the 2nd International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2023, July 7–9, 2023, Chongqing, China

Research Article

Employee Performance Prediction based on the Second-order Stacking Algorithm

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  • @INPROCEEDINGS{10.4108/eai.7-7-2023.2338055,
        author={Yanming  Chen and Xinyu  Lin and Kunye  Zhan},
        title={Employee Performance Prediction based on the Second-order Stacking Algorithm},
        proceedings={Proceedings of the 2nd International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2023, July 7--9, 2023, Chongqing, China},
        publisher={EAI},
        proceedings_a={FFIT},
        year={2023},
        month={10},
        keywords={employee performance second-order stacking algorithm machine learning ensemble learning adaboosting},
        doi={10.4108/eai.7-7-2023.2338055}
    }
    
  • Yanming Chen
    Xinyu Lin
    Kunye Zhan
    Year: 2023
    Employee Performance Prediction based on the Second-order Stacking Algorithm
    FFIT
    EAI
    DOI: 10.4108/eai.7-7-2023.2338055
Yanming Chen1,*, Xinyu Lin2, Kunye Zhan3
  • 1: Shantou University
  • 2: South China Normal University
  • 3: Shenzhen University
*Contact email: 21ymchen@stu.edu.cn

Abstract

This paper attempts to establish an performance prediction model for employees in the field of human resource management based on the second-order stacking algorithm which is an improvement of stacking algorithm. Firstly, the Adaboosting feature importance ranking method is used for feature selection, and then bagging and stacking algorithms are used to establish regression models as control experiments. Finally, a second-order stacking algorithm is used to establish a performance prediction model for employees, achieving minimal error.