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Prediction of Employee Turnover Using Ensemble Learning
Karande S,
Published in Springer Singapore
Volume: 904
Pages: 319 - 327
Employee turnover is now becoming a major problem in IT organizations, telecommunications, and many other industries. Why employees leave the organization is the question rising amongst many HR managers. Employees are the most important assets of an organization. Hiring new employees will always take more efforts and cost rather than retaining the old ones. This paper focuses on finding the key features of voluntary employee turnover and how they can be overcome well before time. The problem is to predict whether an employee will leave or stay based on some metrics. The proposed work will use the application of ensemble learning to solve the problem, rather than focusing on a single classifier algorithm. Each classification model will be assigned with some weight based on the individual predicted accuracy. The ensemble model will calculate the weightage average for the probabilities of the individual classification and based on this weightage average, an employee can be classified. Accurate prediction will help organizations take necessary steps toward controlling retention. © 2019, Springer Nature Singapore Pte Ltd.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing Ambient Communications and Computer Systems
PublisherData powered by TypesetSpringer Singapore
Open AccessNo