The Employee Promotion Decision based on the Randomforest Algorithm and the Analytic Hierarchy Process

Yanming Chen; Xinyu Lin; Kunye Zhan

doi:10.2991/978-2-38476-126-5_185

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The Employee Promotion Decision based on the Randomforest Algorithm and the Analytic Hierarchy Process

Authors

Yanming Chen¹^{, *}, Xinyu Lin²^{, 3}, Kunye Zhan²^{, 3}

¹Shantou University, Shantou, China

²South China Normal University, Guangzhou, China

³Shenzhen University, Shenzhen, China

^*Corresponding author. Email: 21ymchen@stu.edu.cn

Corresponding Author

Yanming Chen

Available Online 31 October 2023.

DOI: 10.2991/978-2-38476-126-5_185 How to use a DOI?
Keywords: Employee Promotion; Randomforest; Analytic Hierarchy Process; Point-biserial; Human Resource Management
Abstract: This paper aims to build an employee promotion decision model based on the Randomforest algorithm and the Analytic Hierarchy Process. Random Undersampling algorithm is applied to resolve the issue of data imbalance and the Point-biserial analysis is employed for the paper to conduct feature filtering after data cleaning and preprocessing. Subsequently, we employ the Randomforest algorithm to establish a classification model for employee promotion, alongside a logistic regression algorithm for comparative purposes. Ultimately, we optimize the decision-making system using the Analytic Hierarchy Process (AHP) to improve its overall efficiency.This model holds significant implications for both employee promotion decision and human resource management.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2023 7th International Seminar on Education, Management and Social Sciences (ISEMSS 2023)
Series: Advances in Social Science, Education and Humanities Research
Publication Date: 31 October 2023
ISBN: 10.2991/978-2-38476-126-5_185
ISSN: 2352-5398
DOI: 10.2991/978-2-38476-126-5_185 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Yanming Chen
AU  - Xinyu Lin
AU  - Kunye Zhan
PY  - 2023
DA  - 2023/10/31
TI  - The Employee Promotion Decision based on the Randomforest Algorithm and the Analytic Hierarchy Process
BT  - Proceedings of the 2023 7th International Seminar on Education, Management and Social Sciences (ISEMSS 2023)
PB  - Atlantis Press
SP  - 1644
EP  - 1653
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-126-5_185
DO  - 10.2991/978-2-38476-126-5_185
ID  - Chen2023
ER  -

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