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Ensemble Classifier-Based Features Ranking on Employee Attrition

Yok-Yen Nguwi*

Nanyang Business School, Nanyang Technological University, 639798, Singapore

* Corresponding Author: Yok-Yen Nguwi. Email: email

Journal on Artificial Intelligence 2022, 4(3), 189-199. https://doi.org/10.32604/jai.2022.034064

Abstract

The departure of good employee incurs direct and indirect cost and impacts for an organization. The direct cost arises from hiring to training of the relevant employee. The replacement time and lost productivity affect the running of business processes. This work presents the use of ensemble classifier to identify important attributes that affects attrition significantly. The data consists of attributes related to job function, education level, satisfaction towards work and working relationship, compensation, and frequency of business travel. Both bagging and boosting classifiers were used for testing. The results show that the selected features (nine selected features) achieve the same result as the full features. The selected features are age, income, working years, source of employment, years since last promotion, salary hike, and business travelling frequency. These features were selected using ensemble classifiers. Satisfaction on work and relationship do not appear to be significant attributes in attrition from ensemble classifier’s results.

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Cite This Article

Y. Nguwi, "Ensemble classifier-based features ranking on employee attrition," Journal on Artificial Intelligence, vol. 4, no.3, pp. 189–199, 2022. https://doi.org/10.32604/jai.2022.034064



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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