The Employee Promotion Decision based on the Randomforest Algorithm and the Analytic Hierarchy Process
- DOI
- 10.2991/978-2-38476-126-5_185How 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.
Cite this article
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 -