A New Approach for Employee Attrition Prediction

Lydia Douaidi () and Hamamache Kheddouci ()
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Lydia Douaidi: LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information - UL2 - Université Lumière - Lyon 2 - ECL - École Centrale de Lyon - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - CNRS - Centre National de la Recherche Scientifique, ESI - École Nationale Supérieure d'Informatique [Alger]
Hamamache Kheddouci: LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information - UL2 - Université Lumière - Lyon 2 - ECL - École Centrale de Lyon - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - CNRS - Centre National de la Recherche Scientifique

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Abstract: Human resources are one of the most important assets in an organization, the success of any business or organization depends on its people achieving goals, meeting deadlines, maintaining quality and keeping customers happy. In a competitive environment, one of the biggest problems that companies face is employee departure or « Employee Attrition ». The automatic prediction of employee attrition has only recently begun to attract the attention of researchers in various industries, as it can predict employee departure and identify the factors that influence employees to change employers. Some AI platforms are developed in order to predict employee attrition, i.e. employees likely to change employer. The objective is to help companies anticipate departures to minimize financial losses due to employee attrition. However, these prediction systems are not generic and are specific to each company. In this context, we are interested in understanding the factors that influence an employee to leave his position. We aim to develop a generic attrition prediction platform that does not depend on the application domain, based on bipartite graph properties and machine learning algorithms.

Keywords: Employee attrition; Bipartite graph; Machine learning (search for similar items in EconPapers)
Date: 2022-09-08
Note: View the original document on HAL open archive server: https://hal.science/hal-05250630v1
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Published in 27th International Conference on Conceptual Structures (ICCS 2022), Sep 2022, Berlin, Germany. pp.115-128, ⟨10.1007/978-3-031-16663-1_9⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05250630

DOI: 10.1007/978-3-031-16663-1_9

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