A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition

Hadeel Alharbi, Obaid Alshammari, Houssem Jerbi, Theodore E. Simos (), Vasilios N. Katsikis, Spyridon D. Mourtas and Romanos D. Sahas
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Hadeel Alharbi: Department of Information and Computer Science, College of Computer Science and Engineering, University of Hail, Hail 2440, Saudi Arabia
Obaid Alshammari: Department of Electrical Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia
Houssem Jerbi: Department of Industrial Engineering, College of Engineering, University of Hail, Hail 2440, Saudi Arabia
Theodore E. Simos: Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
Vasilios N. Katsikis: Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
Spyridon D. Mourtas: Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
Romanos D. Sahas: Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece

Mathematics, 2023, vol. 11, issue 6, 1-17

Abstract: Employee attrition, defined as the voluntary resignation of a subset of a company’s workforce, represents a direct threat to the financial health and overall prosperity of a firm. From lost reputation and sales to the undermining of the company’s long-term strategy and corporate secrets, the effects of employee attrition are multidimensional and, in the absence of thorough planning, may endanger the very existence of the firm. It is thus impeccable in today’s competitive environment that a company acquires tools that enable timely prediction of employee attrition and thus leave room either for retention campaigns or for the formulation of strategical maneuvers that will allow the firm to undergo their replacement process with its economic activity left unscathed. To this end, a weights and structure determination (WASD) neural network utilizing Fresnel cosine integrals in the determination of its activation functions, termed FCI-WASD, is developed through a process of three discrete stages. Those consist of populating the hidden layer with a sufficient number of neurons, fine-tuning the obtained structure through a neuron trimming process, and finally, storing the necessary portions of the network that will allow for its successful future recreation and application. Upon testing the FCI-WASD on two publicly available employee attrition datasets and comparing its performance to that of five popular and well-established classifiers, the vast majority of them coming from MATLAB’s classification learner app, the FCI-WASD demonstrated superior performance with the overall results suggesting that it is a competitive as well as reliable model that may be used with confidence in the task of employee attrition classification.

Keywords: Fresnel integrals; neural networks; WASD; classification; employee attrition; MATLAB (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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