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Varshney, K. R., Chenthamarakshan, V., Fancher, S. W., Wang, J., Fang, D., & Mojsilovic, A. (2014). Predicting Employee Expertise for Talent Management in the Enterprise. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 24-27 August 2014, 1729-1738.
https://doi.org/10.1145/2623330.2623337

has been cited by the following article:

• TITLE: Applied Machine Learning Techniques on Selection and Positioning of Human Resources in the Public Sector

  AUTHORS: Panagiota Pampouktsi, Spyridon Avdimiotis, Manolis Μaragoudakis, Markos Avlonitis

  KEYWORDS: Selection, Positioning, Machine Learning, Assessment Algorithm, Classification

  JOURNAL NAME: Open Journal of Business and Management, Vol.9 No.2, March 10, 2021

  ABSTRACT: Proper selection and positioning of employees is an important issue for strategic human resources management. Within this framework, the aim of the research conducted, was to investigate the most efficient machine learning techniques to support employees’ recruitment and positioning evaluation. Towards this aim, a series of tests were conducted based on classification algorithms concerning employees of the public sector, seeking to predict best fit in workplaces and allocation of employees. Based on the outcome of the administered tests, an algorithm model was built to assist the decision support system of employees’ recruitment and assessment. The primary findings of the present research could lead to the argument that the adoption of the Employees’ Evaluation for Recruitment and Promotion Algorithm Model (EERPAM) will significantly improve the objectivity of employees’ recruitment and positioning procedures.