How to cite this paper
Alsubaie, F & Aldoukhi, M. (2024). Using machine learning algorithms with improved accuracy to analyze and predict employee attrition.Decision Science Letters , 13(1), 1-18.
Refrences
Al-Darraji, S., Honi, D. G., Fallucchi, F., Abdulsada, A. I., Giuliano, R., & Abdulmalik, H. A. (2021). Employee attrition prediction using deep neural networks. Computers, 10(11), 1–11. https://doi.org/10.3390/computers10110141
Alao, D. A. B. A., & Adeyemo, A. B. (2013). Analyzing employee attrition using decision tree algorithms. Information Systems & Development Informatics, 4(1), 17–28.
Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study. International Journal on Semantic Web and Information Systems, 18(1), 1–24. https://doi.org/10.4018/IJSWIS.297032
Bhartiya, N., Jannu, S., Shukla, P., & Chapaneri, R. (2019). Employee Attrition Prediction Using Classification Models. 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019. https://doi.org/10.1109/I2CT45611.2019.9033784
Branham, L. (2005). Planning to become an employer of choice. Journal of Organizational Excellence, 24(3), 57–68. https://doi.org/10.1002/joe.20060
Breiman, L. (1996). Out-of-Bag Estimation. In Statistics Department: University of California Berkeley.
Ceriani, L., & Verme, P. (2012). The origins of the Gini index: Extracts from Variabilità e Mutabilità (1912) by Corrado Gini. Journal of Economic Inequality, 10(3), 421–443. https://doi.org/10.1007/s10888-011-9188-x
Dalton, D. R., & Mesch, D. J. (1990). The Impact of Flexible Scheduling on Employee Attendance and Turnover. Administrative Science Quarterly, 370–387. Retrieved from http://www.jstor.org/stable/pdf/3150242.pdf?_=1467266017307
Desboulets, L. D. D. (2018). A review on variable selection in regression analysis. Econometrics. https://doi.org/10.3390/econometrics6040045
Fallucchi, F., Coladangelo, M., Giuliano, R., & De Luca, E. W. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4). https://doi.org/10.3390/computers9040086
Gaurav, A., Gupta, B. B., & Panigrahi, P. K. (2023). A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system. Enterprise Information Systems, 17(3). https://doi.org/10.1080/17517575.2021.2023764
Here’s what your turnover and retention rates should look like. (n.d.). Retrieved October 14, 2022, from https://www.ceridian.com/blog/turnover- and-retention-rates-benchmark (accessed
Joseph, R., Udupa, S., Jangale, S., Kotkar, K., & Pawar, P. (2021). Employee attrition using machine learning and depression analysis. Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021. https://doi.org/10.1109/ICICCS51141.2021.9432259
Lazzari, M., Alvarez, J. M., & Ruggieri, S. (2022). Predicting and explaining employee turnover intention. International Journal of Data Science and Analytics, 14(3), 279–292. https://doi.org/10.1007/s41060-022-00329-w
Liu, J. L. (2014). Main causes of voluntary employee turnover: A study of factors and their relationship with expectations and preferences. University Of Chile.
Nagadevara, V., & Srinivasan, V. (2007). Early Prediction of Employee Attrition in Software Companies-Application of Data Mining Techniques. The 10th International Conference of the Society of Global Business and Economic Development.
Najafi-Zangeneh, S., Shams-Gharneh, N., Arjomandi-Nezhad, A., & Zolfani, S. H. (2021). An improved machine learning-based employees attrition prediction framework with emphasis on feature selection. Mathematics, 9(11). https://doi.org/10.3390/math9111226
Ponnuru, S., Merugumala, G., Padigala, S., Vanga, R., & Kantapalli, B. (2020). Employee Attrition Prediction using Logistic Regression. International Journal for Research in Applied Science and Engineering Technology, 8(5), 2871–2875. https://doi.org/10.22214/ijraset.2020.5481
Qutub, A., Al-Mehmadi, A., Al-Hssan, M., Aljohani, R., & Alghamdi, H. S. (2021). Prediction of Employee Attrition Using Machine Learning and Ensemble Methods. International Journal of Machine Learning and Computing, 11(2), 110–114. https://doi.org/10.18178/ijmlc.2021.11.2.1022
Rombaut, E., & Guerry, M. A. (2018). Predicting voluntary turnover through human resources database analysis. Management Research Review, 41(1), 96–112. https://doi.org/10.1108/MRR-04-2017-0098
Subhash Pavan. (2017). IBM HR Analytics Employee Attrition & Performance. Retrieved from https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset
Subhashini, M., & Gopinath, R. (2020). Employee Attrition Prediction in Industry Using Machine Learning Techniques. International Journal of Advanced Research in Engineering and Technology, 11(12), 3329–3341. Retrieved from https://doi.org/10.34218/IJARET.11.12.2020.313
Vasa, J., & Masrani, K. (2019). Foreseeing employee attritions using diverse data mining strategies. International Journal of Recent Technology and Engineering, 8(3), 620–626. https://doi.org/10.35940/ijrte.B2406.098319
Wassan, S., Suhail, B., Mubeen, R., Raj, B., Agarwal, U., Khatri, E., … Dhiman, G. (2022). Gradient Boosting for Health IoT Federated Learning. Sustainability, 14(24). https://doi.org/10.3390/su142416842
Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., & Zhu, X. (2018). Employee turnover prediction with machine learning: A reliable approach. In Advances in Intelligent Systems and Computing (Vol. 2). https://doi.org/10.1007/978-3-030-01057-7_56
Alao, D. A. B. A., & Adeyemo, A. B. (2013). Analyzing employee attrition using decision tree algorithms. Information Systems & Development Informatics, 4(1), 17–28.
Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study. International Journal on Semantic Web and Information Systems, 18(1), 1–24. https://doi.org/10.4018/IJSWIS.297032
Bhartiya, N., Jannu, S., Shukla, P., & Chapaneri, R. (2019). Employee Attrition Prediction Using Classification Models. 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019. https://doi.org/10.1109/I2CT45611.2019.9033784
Branham, L. (2005). Planning to become an employer of choice. Journal of Organizational Excellence, 24(3), 57–68. https://doi.org/10.1002/joe.20060
Breiman, L. (1996). Out-of-Bag Estimation. In Statistics Department: University of California Berkeley.
Ceriani, L., & Verme, P. (2012). The origins of the Gini index: Extracts from Variabilità e Mutabilità (1912) by Corrado Gini. Journal of Economic Inequality, 10(3), 421–443. https://doi.org/10.1007/s10888-011-9188-x
Dalton, D. R., & Mesch, D. J. (1990). The Impact of Flexible Scheduling on Employee Attendance and Turnover. Administrative Science Quarterly, 370–387. Retrieved from http://www.jstor.org/stable/pdf/3150242.pdf?_=1467266017307
Desboulets, L. D. D. (2018). A review on variable selection in regression analysis. Econometrics. https://doi.org/10.3390/econometrics6040045
Fallucchi, F., Coladangelo, M., Giuliano, R., & De Luca, E. W. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4). https://doi.org/10.3390/computers9040086
Gaurav, A., Gupta, B. B., & Panigrahi, P. K. (2023). A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system. Enterprise Information Systems, 17(3). https://doi.org/10.1080/17517575.2021.2023764
Here’s what your turnover and retention rates should look like. (n.d.). Retrieved October 14, 2022, from https://www.ceridian.com/blog/turnover- and-retention-rates-benchmark (accessed
Joseph, R., Udupa, S., Jangale, S., Kotkar, K., & Pawar, P. (2021). Employee attrition using machine learning and depression analysis. Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021. https://doi.org/10.1109/ICICCS51141.2021.9432259
Lazzari, M., Alvarez, J. M., & Ruggieri, S. (2022). Predicting and explaining employee turnover intention. International Journal of Data Science and Analytics, 14(3), 279–292. https://doi.org/10.1007/s41060-022-00329-w
Liu, J. L. (2014). Main causes of voluntary employee turnover: A study of factors and their relationship with expectations and preferences. University Of Chile.
Nagadevara, V., & Srinivasan, V. (2007). Early Prediction of Employee Attrition in Software Companies-Application of Data Mining Techniques. The 10th International Conference of the Society of Global Business and Economic Development.
Najafi-Zangeneh, S., Shams-Gharneh, N., Arjomandi-Nezhad, A., & Zolfani, S. H. (2021). An improved machine learning-based employees attrition prediction framework with emphasis on feature selection. Mathematics, 9(11). https://doi.org/10.3390/math9111226
Ponnuru, S., Merugumala, G., Padigala, S., Vanga, R., & Kantapalli, B. (2020). Employee Attrition Prediction using Logistic Regression. International Journal for Research in Applied Science and Engineering Technology, 8(5), 2871–2875. https://doi.org/10.22214/ijraset.2020.5481
Qutub, A., Al-Mehmadi, A., Al-Hssan, M., Aljohani, R., & Alghamdi, H. S. (2021). Prediction of Employee Attrition Using Machine Learning and Ensemble Methods. International Journal of Machine Learning and Computing, 11(2), 110–114. https://doi.org/10.18178/ijmlc.2021.11.2.1022
Rombaut, E., & Guerry, M. A. (2018). Predicting voluntary turnover through human resources database analysis. Management Research Review, 41(1), 96–112. https://doi.org/10.1108/MRR-04-2017-0098
Subhash Pavan. (2017). IBM HR Analytics Employee Attrition & Performance. Retrieved from https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset
Subhashini, M., & Gopinath, R. (2020). Employee Attrition Prediction in Industry Using Machine Learning Techniques. International Journal of Advanced Research in Engineering and Technology, 11(12), 3329–3341. Retrieved from https://doi.org/10.34218/IJARET.11.12.2020.313
Vasa, J., & Masrani, K. (2019). Foreseeing employee attritions using diverse data mining strategies. International Journal of Recent Technology and Engineering, 8(3), 620–626. https://doi.org/10.35940/ijrte.B2406.098319
Wassan, S., Suhail, B., Mubeen, R., Raj, B., Agarwal, U., Khatri, E., … Dhiman, G. (2022). Gradient Boosting for Health IoT Federated Learning. Sustainability, 14(24). https://doi.org/10.3390/su142416842
Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., & Zhu, X. (2018). Employee turnover prediction with machine learning: A reliable approach. In Advances in Intelligent Systems and Computing (Vol. 2). https://doi.org/10.1007/978-3-030-01057-7_56