research-article

Prediction of Employee Promotion Based on Personal Basic Features and Post Features

Authors:

Wei JiangAuthors Info & Claims

ICDPA 2018: Proceedings of the International Conference on Data Processing and Applications

Pages 5 - 10

https://doi.org/10.1145/3224207.3224210

Published: 12 May 2018 Publication History

Abstract

Promotion is the focus of human resource management research. Because there are few researches about the mining of promotion features in existing studies, this paper uses the data of a Chinese state-owned enterprise, constructs a number of features and applies machine learning methods to predict employee promotion. Firstly, we build personal basic features and post features based on five strategies. Secondly, the correlation analysis is conducted to preliminarily explore the associations between some features and promotion. Then, the model learning and testing are carried out. Experimental results show that the random forest model performs best, which verifies the validity of features. Finally, we calculate the Gini importance of each feature to further analyze its influence on staff promotion. It is found that post features have a higher impact on promotion compared with personal basic features. Among all the features, the working years, the number of different positions and the highest department level greatly affect employee promotion.

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Cited By

K. Durves Mohideen S. Prasanna (2024)Predicting Employee Promotion using Machine LearningInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJETIR-1240(217-220)Online publication date: 10-Jul-2024
https://doi.org/10.48175/IJETIR-1240
Ibrir YÇavur M(2024)Forecasting Employees’ Promotion Based on Personal Indicators by Using a Machine Learning AlgorithmUluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi10.33461/uybisbbd.14714998:2(75-98)Online publication date: 30-Dec-2024
https://doi.org/10.33461/uybisbbd.1471499
Fabris ABaranowska NDennis MGraus DHacker PSaldivar JZuiderveen Borgesius FBiega A(2024)Fairness and Bias in Algorithmic Hiring: A Multidisciplinary SurveyACM Transactions on Intelligent Systems and Technology10.1145/369645716:1(1-54)Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1145/3696457
Show More Cited By

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Prediction of Employee Promotion Based on Personal Basic Features and Post Features

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cover image ACM Other conferences

ICDPA 2018: Proceedings of the International Conference on Data Processing and Applications

May 2018

73 pages

ISBN:9781450364188

DOI:10.1145/3224207

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Peking University: Peking University
Guangdong University of Technology: Guangdong University of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 May 2018

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ICDPA 2018

ICDPA 2018: ICDPA 2018, International Conference on Data Processing and Applications

May 12 - 14, 2018

Guangdong, China

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Cited By

K. Durves Mohideen S. Prasanna (2024)Predicting Employee Promotion using Machine LearningInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJETIR-1240(217-220)Online publication date: 10-Jul-2024
https://doi.org/10.48175/IJETIR-1240
Ibrir YÇavur M(2024)Forecasting Employees’ Promotion Based on Personal Indicators by Using a Machine Learning AlgorithmUluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi10.33461/uybisbbd.14714998:2(75-98)Online publication date: 30-Dec-2024
https://doi.org/10.33461/uybisbbd.1471499
Fabris ABaranowska NDennis MGraus DHacker PSaldivar JZuiderveen Borgesius FBiega A(2024)Fairness and Bias in Algorithmic Hiring: A Multidisciplinary SurveyACM Transactions on Intelligent Systems and Technology10.1145/369645716:1(1-54)Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1145/3696457
Wang KRen YYang YWang SChen Y(2024)Prediction and Analysis of Employee Promotions Using Machine Learning2024 International Conference on Innovation, Knowledge, and Management (ICIKM)10.1109/ICIKM63301.2024.00011(15-22)Online publication date: 21-Jun-2024
https://doi.org/10.1109/ICIKM63301.2024.00011
Tuhairwe JMukisa JAl-Absi A(2024)Web-Based Employee Promotion Prediction Using Random Forest ClassifierProceedings of 3rd International Conference on Smart Computing and Cyber Security10.1007/978-981-97-0573-3_37(459-475)Online publication date: 28-Jul-2024
https://doi.org/10.1007/978-981-97-0573-3_37
Bouhsaien LAzmani ABenallou I(2024)A Combined AHP-TOPSIS Model for the Selection of Employees for PromotionSmart Applications and Data Analysis10.1007/978-3-031-77040-1_13(175-189)Online publication date: 24-Dec-2024
https://doi.org/10.1007/978-3-031-77040-1_13
Thakurta R(2024)A work system theory perspective on talent management and systemsSystems Research and Behavioral Science10.1002/sres.3007Online publication date: 3-Apr-2024
https://doi.org/10.1002/sres.3007
Ilwani MNassreddine GYounis J(2023)Machine Learning Application on Employee ‎PromotionMesopotamian Journal of Computer Science10.58496/MJCSC/2023/013(106-120)Online publication date: 7-Jun-2023
https://doi.org/10.58496/MJCSC/2023/013
Kediya SSomnathe TSurya AGarg NAli AGudadhe A(2023)Machine Learning Algorithms for Unbalanced Dataset Promotion Prediction for Employees2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI)10.1109/ICCSAI59793.2023.10421250(523-526)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICCSAI59793.2023.10421250
Hoq MChandra PHossain SGhosh SAhamad MOion MFoyes A(2023)Predict Employee Promotion Using Supervised Classification ApproachesInnovations in Data Analytics10.1007/978-981-99-0550-8_14(181-192)Online publication date: 1-Jun-2023
https://doi.org/10.1007/978-981-99-0550-8_14
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