research-article

Analyzing Employee Turnover Based on Job Skills

Authors:

Guosheng ChenAuthors Info & Claims

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

Pages 16 - 21

https://doi.org/10.1145/3224207.3224209

Published: 12 May 2018 Publication History

Abstract

Turnover study is an important part of human resource management. Most former researches use methods which only can access limited information to investigate employee turnover. So, technologies like data mining and machine learning are adopted in this paper to explore turnover more fully and efficiently. Features of job skills are extracted from a large employee dataset and then divided into specific sub-characteristics. Next, we choose four classification algorithms to establish our prediction model and forecast staff turnover. By revealing what extent that turnover can be predicted based on job skills, the correlation between them can be learned. The experimental results show that features of job skills are able to accurately predict turnover, and expertise is the most relevant factor. The study further provides decision supports for employee development, personnel selection, staff training and talent retention.

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Cited By

B SKarim Shaikh IJagdish Patil PSethumadhavan RPreetha MPatil H(2023)Predictive Analysis of Employee Turnover in IT Using a Hybrid CRF-BiLSTM and CNN Model2023 International Conference on Sustainable Communication Networks and Application (ICSCNA)10.1109/ICSCNA58489.2023.10370093(914-919)Online publication date: 15-Nov-2023
https://doi.org/10.1109/ICSCNA58489.2023.10370093
Cárdenas López PTabares Betancur M(2023)Discovering Key Aspects to Reduce Employee Turnover Using a Predictive ModelAdvances in Computing10.1007/978-3-031-47372-2_30(380-395)Online publication date: 14-Nov-2023
https://doi.org/10.1007/978-3-031-47372-2_30
Patel KSheth KMehta DTanwar SFlorea BTaralunga DAltameem AAltameem TSharma R(2022)RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low PerformersMathematics10.3390/math1019371410:19(3714)Online publication date: 10-Oct-2022
https://doi.org/10.3390/math10193714
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Analyzing Employee Turnover Based on Job Skills

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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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National Natural Science Foundation of China

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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

B SKarim Shaikh IJagdish Patil PSethumadhavan RPreetha MPatil H(2023)Predictive Analysis of Employee Turnover in IT Using a Hybrid CRF-BiLSTM and CNN Model2023 International Conference on Sustainable Communication Networks and Application (ICSCNA)10.1109/ICSCNA58489.2023.10370093(914-919)Online publication date: 15-Nov-2023
https://doi.org/10.1109/ICSCNA58489.2023.10370093
Cárdenas López PTabares Betancur M(2023)Discovering Key Aspects to Reduce Employee Turnover Using a Predictive ModelAdvances in Computing10.1007/978-3-031-47372-2_30(380-395)Online publication date: 14-Nov-2023
https://doi.org/10.1007/978-3-031-47372-2_30
Patel KSheth KMehta DTanwar SFlorea BTaralunga DAltameem AAltameem TSharma R(2022)RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low PerformersMathematics10.3390/math1019371410:19(3714)Online publication date: 10-Oct-2022
https://doi.org/10.3390/math10193714
Nosratabadi SZahed RPonkratov VKostyrin E(2022)Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature ReviewOrganizacija10.2478/orga-2022-001255:3(181-198)Online publication date: 23-Sep-2022
https://doi.org/10.2478/orga-2022-0012
Kaushal NKaurav RSivathanu BKaushik N(2021)Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysisManagement Review Quarterly10.1007/s11301-021-00249-273:2(455-493)Online publication date: 29-Nov-2021
https://doi.org/10.1007/s11301-021-00249-2
Jin ZShang JZhu QLing CXie WQiang B(2020)RFRSF: Employee Turnover Prediction Based on Random Forests and Survival AnalysisWeb Information Systems Engineering – WISE 202010.1007/978-3-030-62008-0_35(503-515)Online publication date: 21-Oct-2020
https://doi.org/10.1007/978-3-030-62008-0_35
Zhu QShang JCai XJiang LLiu FQiang B(2019)CoxRF: Employee Turnover Prediction Based on Survival Analysis2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00212(1123-1130)Online publication date: Aug-2019
https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00212

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