A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition
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...
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/6/1506 |
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| author | Hadeel Alharbi Obaid Alshammari Houssem Jerbi Theodore E. Simos Vasilios N. Katsikis Spyridon D. Mourtas Romanos D. Sahas |
| author_facet | Hadeel Alharbi Obaid Alshammari Houssem Jerbi Theodore E. Simos Vasilios N. Katsikis Spyridon D. Mourtas Romanos D. Sahas |
| author_sort | Hadeel Alharbi |
| collection | DOAJ |
| description | 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. |
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| format | Article |
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| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T06:13:03Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-19bfb7ee960141979feaa269039258742023-11-17T12:29:35ZengMDPI AGMathematics2227-73902023-03-01116150610.3390/math11061506A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee AttritionHadeel Alharbi0Obaid Alshammari1Houssem Jerbi2Theodore E. Simos3Vasilios N. Katsikis4Spyridon D. Mourtas5Romanos D. Sahas6Department of Information and Computer Science, College of Computer Science and Engineering, University of Hail, Hail 2440, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, University of Hail, Hail 1234, Saudi ArabiaDepartment of Industrial Engineering, College of Engineering, University of Hail, Hail 2440, Saudi ArabiaDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, TaiwanDepartment of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, GreeceDepartment of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, GreeceDepartment of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, GreeceEmployee 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.https://www.mdpi.com/2227-7390/11/6/1506Fresnel integralsneural networksWASDclassificationemployee attritionMATLAB |
| spellingShingle | Hadeel Alharbi Obaid Alshammari Houssem Jerbi Theodore E. Simos Vasilios N. Katsikis Spyridon D. Mourtas Romanos D. Sahas A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition Mathematics Fresnel integrals neural networks WASD classification employee attrition MATLAB |
| title | A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition |
| title_full | A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition |
| title_fullStr | A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition |
| title_full_unstemmed | A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition |
| title_short | A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition |
| title_sort | fresnel cosine integral wasd neural network for the classification of employee attrition |
| topic | Fresnel integrals neural networks WASD classification employee attrition MATLAB |
| url | https://www.mdpi.com/2227-7390/11/6/1506 |
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