Çalışan yıpranmasını tahmin etmede analitik bir yaklaşım: Topluluk öğrenme yöntemi

Yıl 2024, , 150 - 160, 21.10.2024

Mustafa Yurtsever

https://doi.org/10.33707/akuiibfd.1462567

Öz

Şirketler, profesyonel çalışanlarının ayrılmasını engelleyerek işe alım ve eğitim maliyetlerini azaltmak için çeşitli önlemler ararlar. Belirli bir çalışanın ayrılıp ayrılmayacağını önceden tahmin etmek, şirketin bu tür kayıpları minimize etmek için gereken adımları atmasını sağlar. Bu nedenle, çalışanların ayrılma olasılığını önceden tahmin etmek, işverenlere stratejik kararlar almalarında yardımcı olabilir. Çalışan yıpranması bu noktada çalışanların işten ayrılma niyetlerini anlama konusunda belirleyici olabilmektedir. Topluluk öğrenme modelleri, birden fazla algoritmanın çeşitli görüşlerini birleştirerek daha doğru ve güvenilir sonuçlar elde etme potansiyeline sahiptir. Bu çalışmada, 1.470 kayıttan oluşan IBM şirketi Watson Analytics tarafından hazırlanan çalışanların yıpranma durumunu gösteren veri seti kullanılmıştır. Bu amaçla, Rastgele Orman, Destek Vektör Makineleri, Çok Katmanlı Algılayıcı modeli ve iki farklı topluluk öğrenme modeli kullanılarak performansları değerlendirilmiştir. Sınıf dengesizliğini aşmak için adaptif sentetik veri üretimi (ADASYN) yaklaşımı kullanılmıştır. Sonuç olarak, ADASYN kullanılarak oluşturulan dengeli bir veri kümesi ile eğitilen yığın topluluk öğrenme modeli ile 0.96 doğruluk oranı elde etmiştir.

Anahtar Kelimeler

Çalışan Yıpranması, Tahminleme, Topluluk Öğrenmesi Yöntemleri

An analytical approach to predicting employee attrition: Ensemble learning method

Öz

Companies seek various measures to prevent the departure of professional employees and thereby reduce recruitment and training costs. Predicting whether a specific employee will leave or not enables the company to take the necessary steps to minimize such losses. Therefore, predicting the likelihood of employee attrition in advance can assist employers in making strategic decisions. Employee attrition plays a crucial role in understanding employees' intentions to leave their jobs. Ensemble learning models have the potential to achieve more accurate and reliable results by combining various perspectives of multiple algorithms. In this study, a dataset indicating the attrition status of employees, prepared by IBM Watson Analytics and consisting of 1,470 records, was utilized. For this purpose, the performances were evaluated using Random Forest, Support Vector Machines, Multi-Layer Perceptron model, and two different ensemble learning models. To overcome class imbalance, the adaptive synthetic data generation (ADASYN) approach was used. As a result, a stacked ensemble learning model trained on a balanced dataset created using ADASYN achieved an accuracy rate of 0.96.

Anahtar Kelimeler

Employee Attrition, Prediction, Ensemble Learning Methods

Kaynakça

• 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), 141. https://doi.org/10.3390/computers10110141
• Aldulaimi, S. H., Abdeldayem, M. M., Mowafak, B. M., & Abdulaziz, M. M. (2021). Experimental perspective of artificial intelligence technology in human resources management. In Hamdan, A., Hassanien, A. E., Khamis, R., Alareeni, B., Razzaque, A., & Awwad, B. (Eds.), Applications of artificial intelligence in business, education and healthcare (Vol. 954, pp. 605–619). Springer. https://doi.org/10.1007/978-3-030-72080-3_26
• Alduayj, S. S., & Rajpoot, K. (2018). Predicting employee attrition using machine learning. In 2018 International Conference on Innovations in Information Technology (IIT) (pp. 93–98). IEEE. https://doi.org/10.1109/INNOVATIONS.2018.8605976
• Alao, D. A. B. A., & Adeyemo, A. B. (2013). Analyzing employee attrition using decision tree algorithms. Computing, Information Systems, Development Informatics and Allied Research Journal, 4(1), 17–28.
• Alsheref, F. K., Fattoh, I. E., & M. Ead, W. (2022). Automated prediction of employee attrition using ensemble model based on machine learning algorithms. Computational Intelligence and Neuroscience, 2022(1), 7728668. https://doi.org/10.1155/2022/7728668
• Alshiddy, M. S., & Aljaber, B. N. (2023). Employee attrition prediction using nested ensemble learning techniques. International Journal of Advanced Computer Science and Applications, 14(7), 932–938. https://doi.org/10.14569/IJACSA.2023.01400712
• 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. https://doi.org/10.5267/j.dsl.2023.12.006
• Avrahami, D., Pessach, D., Singer, G., & Chalutz Ben-Gal, H. (2022). A human resources analytics and machine-learning examination of turnover: Implications for theory and practice. International Journal of Manpower, 43(6), 1405–1424. https://doi.org/10.1108/IJM-12-2020-0548
• Barpanda, S., & Athira, S. (2022). Cause of attrition in an information technology-enabled services company: A triangulation approach. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 13(1), 1–22. https://doi.org/10.4018/IJHCITP.20220101.oa1
• Bhatta, S., Zaman, I. U., Raisa, N., Fahim, S. I., & Momen, S. (2022, April). Machine learning approach to predicting attrition among employees at work. In Computer Science On-line Conference (pp. 285–294). Springer International Publishing. https://doi.org/10.1007/978-3-030-93515-3_25
• Breiman, L. (2001). Random forests. Machine Learning, 45(5), 5–32. https://doi.org/10.1023/A:1010933404324
• Buntak, K., Kovačić, M., & Mutavdžija, M. (2021). Application of artificial intelligence in the business. International Journal for Quality Research, 15(2), 403. https://doi.org/10.24874/IJQR15.02-03
• Çelik, U. (2019). Veri madenciliği yöntemleri ile iş yaşam dengesinde yıpranma durumu tahmini. Journal of Management and Economics Research, 17(1), 63–76. https://doi.org/10.11611/yead.519923
• Chung, D., Yun, J., Lee, J., & Jeon, Y. (2023). Predictive model of employee attrition based on stacking ensemble learning. Expert Systems with Applications, 215, 119364. https://doi.org/10.1016/j.eswa.2022.119364
• Clark, A. E. (2001). What really matters in a job? Hedonic measurement using quit data. Labour Economics, 8(2), 223–242. https://doi.org/10.1016/S0927-5371(00)00041-9
• Douaidi, L., & Kheddouci, H. (2022, September). A new approach for employee attrition prediction. In International Conference on Conceptual Structures (pp. 115–128). Springer International Publishing. https://doi.org/10.1007/978-3-030-76294-0_9
• El-Rayes, N., Fang, M., Smith, M., & Taylor, S. M. (2020). Predicting employee attrition using tree-based models. International Journal of Organizational Analysis, 28(6), 1273–1291. https://doi.org/10.1108/IJOA-10-2019-1903
• Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
• Fallucchi, F., Coladangelo, M., Giuliano, R., & De Luca, E. W. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4), 86. https://doi.org/10.3390/computers9040086
• Frye, A., Boomhower, C., Smith, M., Vitovsky, L., & Fabricant, S. (2018). Employee attrition: What makes an employee quit?. SMU Data Science Review, 1(1), 9.
• Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14–15), 2627–2636. https://doi.org/10.1016/S1352-2310(97)00447-0
• Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590–1610. https://doi.org/10.1108/IJPPM-08-2020-0427
• Gosain, A., & Sardana, S. (2017). Handling class imbalance problem using oversampling techniques: A review. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 79–85). IEEE. https://doi.org/10.1109/ICACCI.2017.8125865
• Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and Their Applications, 13(4), 18–28. https://doi.org/10.1109/5254.708428
• Hoffman, M., & Tadelis, S. (2021). People management skills, employee attrition, and manager rewards: An empirical analysis. Journal of Political Economy, 129(1), 243–285. https://doi.org/10.1086/712436
• IBM HR Analytics Employee. (2017). IBM HR Analytics Employee Dataset. Kaggle. https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset
• Jain, P. K., Jain, M., & Pamula, R. (2020). Explaining and predicting employees’ attrition: A machine learning approach. SN Applied Sciences, 2(4), 757. https://doi.org/10.1007/s42452-020-2519-4
• Kaya, İ. E., & Korkmaz, O. (2021). Machine learning approach for predicting employee attrition and factors leading to attrition. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(4), 913–928. https://doi.org/10.21605/cukurovaumfd.1040487
• Khalid, S. M., Rashid, S., & Ullah, R. (2022). Employee retention prediction using machine learning techniques: A review of the state of the art. International Journal of Electrical and Computer Engineering, 12(4), 4498–4506. https://doi.org/10.11591/ijece.v12i4.9440
• Khare, R., Kaloya, D., Choudhary, C. K., & Gupta, G. (2011, January). Employee attrition risk assessment using logistic regression analysis. In Proceedings of the International Conference Advanced Data Analytics Business Analytics Intelligence (pp. 1–33).
• Kumar, N., & Yakhlef, A. (2016). Managing business-to-business relationships under conditions of employee attrition: A transparency approach. Industrial Marketing Management, 56, 143–155. https://doi.org/10.1016/j.indmarman.2016.01.002
• Oruç, E. (2020). Örgütsel yıpranma üzerine kuramsal bir inceleme. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 66, 319–334.
• Oshagbemi, T. (2003). Personal correlates of job satisfaction: Empirical evidence from UK universities. International Journal of Social Economics, 30(12), 1210–1232. https://doi.org/10.1108/03068290310504380
• Raza, A., Munir, K., Almutairi, M., Younas, F., & Fareed, M. M. S. (2022). Predicting employee attrition using machine learning approaches. Applied Sciences, 12(13), 6424. https://doi.org/10.3390/app12136424
• Rutherford, M. W., Buller, P. F., & McMullen, P. R. (2003). Human resource management problems over the life cycle of small to medium-sized firms. Human Resource Management, 42(4), 321–335. https://doi.org/10.1002/hrm.10093
• Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1249. https://doi.org/10.1002/widm.1249
• Sriram, K. V., Joseph, J., Mathew, A. O., & Rai, A. S. (2019). Factors affecting high employee attrition in manufacturing firms–A case study. Calitatea, 20(169), 23–28.
• Srivastava, D. K., & Nair, P. (2018). Employee attrition analysis using predictive techniques. In Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1 (pp. 293–300). Springer International Publishing. https://doi.org/10.1007/978-3-319-98516-7_32
• Subramony, M., & Holtom, B. C. (2012). The long-term influence of service employee attrition on customer outcomes and profits. Journal of Service Research, 15(4), 460–473. https://doi.org/10.1177/1094670512452792
• Suthaharan, S. (2016). Support vector machine. In Machine Learning Models and Algorithms for Big Data Classification (Vol. 36, pp. 393–405). Springer. https://doi.org/10.1007/978-1-4899-7641-3_9
• Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295–2329. https://doi.org/10.1109/JPROC.2017.2763201
• Wilson, E., & Tufts, D. W. (1994). Multilayer perceptron design algorithm. In Proceedings of IEEE Workshop on Neural Networks for Signal Processing (pp. 61–68). IEEE. https://doi.org/10.1109/NNSP.1994.336191
• Yadav, S., Jain, A., & Singh, D. (2018, December). Early prediction of employee attrition using data mining techniques. In 2018 IEEE 8th International Advance Computing Conference (IACC) (pp. 349–354). IEEE. https://doi.org/10.1109/IACC.2018.8710222
• Yahia, N. B., Hlel, J., & Colomo-Palacios, R. (2021). From big data to deep data to support people analytics for employee attrition prediction. IEEE Access, 9, 60447–60458. https://doi.org/10.1109/ACCESS.2021.3074559