A machine learning-based analytical framework for employee turnover prediction

Xinlei Wang and Jianing Zhi

Journal of Management Analytics, 2021, vol. 8, issue 3, 351-370

Abstract: Employee turnover (ET) can cause severe consequences to a company, which are hard to be replaced or rebuilt. It is thus crucial to develop an intelligent system that can accurately predict the likelihood of ET, allowing the human resource management team to take pro-active action for retention or plan for succession. However, building such a system faces challenges due to the variety of influential human factors, the lack of training data, and the large pool of candidate models to choose from. Solutions offered by existing studies only adopt essential learning strategies. To fill this methodological gap, we propose a machine learning-based analytical framework that adopts a streamlined approach to feature engineering, model training and validation, and ensemble learning towards building an accurate and robust predictive model. The proposed framework is evaluated on two representative datasets with different sizes and feature settings. Results demonstrate the superior performance of the final model produced by our framework.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/23270012.2021.1961318 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:351-370

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjma20

DOI: 10.1080/23270012.2021.1961318

Access Statistics for this article

Journal of Management Analytics is currently edited by Li Xu

More articles in Journal of Management Analytics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().