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A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning

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Abstract

Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms.

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References

  • Afonja, T. (2017). Accuracy paradox, towards data science. https://towardsdatascience.com/accuracy-paradox-897a69e2dd9b, Online: Stand 27. July 2020.

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Article  Google Scholar 

  • Brown, D., & Wilson, S. (2007). The black books of outsourcing: How to manage the changes, challenges, and opportunities. Wiley.

  • Budhwar, P.S., Varma, A., Singh, V., & Dhar, R. (2006). HRM systems of indian call centres: an exploratory study. The International Journal of Human Resource Management, 17(5), 881–897. https://doi.org/10.1080/09585190600640976.

    Article  Google Scholar 

  • Chen, T., He, T., Benesty, M., Khotilovich, V., & Tang, Y. (2015). Xgboost: extreme gradient boosting. R package version 04-2 pp 1–4.

  • Chicco, D., & Jurman, G. (2020). The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6.

    Article  Google Scholar 

  • Chien, C.F., & Chen, L.F. (2008). Data mining to improve personnel selection and enhance human capital: a case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290. https://doi.org/10.1016/j.eswa.2006.09.003.

    Article  MathSciNet  Google Scholar 

  • Chu, A.T.W., Kalaba, R.E., & Spingarn, K. (1979). A comparison of two methods for determining the weights of belonging to fuzzy sets. Journal of Optimization Theory and Applications, 27(4), 531–538. https://doi.org/10.1007/bf00933438.

    Article  MathSciNet  MATH  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support vector machine. Machine Learning, 20(3), 273–297.

    MATH  Google Scholar 

  • Dolatabadi, S.H., & Keynia, F. (2017). Designing of customer and employee churn prediction model based on data mining method and neural predictor. In 2017 2nd International Conference on Computer and Communication Systems (ICCCS). https://doi.org/10.1109/ccoms.2017.8075270: IEEE.

  • Fan, C.Y., Fan, P.S., Chan, T.Y., & Chang, S.H. (2012). Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Systems with Applications, 39(10), 8844–8851. https://doi.org/10.1016/j.eswa.2012.02.005.

    Article  Google Scholar 

  • Fazlollahtabar, H. (2010). A subjective framework for seat comfort based on a heuristic multi criteria decision making technique and anthropometry. Applied Ergonomics, 42(1), 16–28. https://doi.org/10.1016/j.apergo.2010.04.004.

    Article  Google Scholar 

  • Fisher, A., Rudin, C., & Dominici, F. (2018). All models are wrong but many are useful: Variable importance for black-box, proprietary, or misspecified prediction models, using model class reliance. arXiv:180101489.

  • Frederiksen, A. (2017). Job satisfaction and employee turnover: a firm-level perspective. German Journal of Human Resource Management, 31(2), 132–161.

    Article  Google Scholar 

  • Frierson, J., & Si, D. (2018). Who’s next: Evaluating attrition with machine learning algorithms and survival analysis. In Big data – BigData 2018. https://doi.org/10.1007/978-3-319-94301-5_19 (pp. 251–259): Springer International Publishing.

  • Gao, X., Wen, J., & Zhang, C. (2019). An improved random forest algorithm for predicting employee turnover. Mathematical Problems in Engineering, 2019, 1–12. https://doi.org/10.1155/2019/4140707.

    Article  Google Scholar 

  • Ghasemaghaei, M., & Calic, G. (2019). Can big data improve firm decision quality? the role of data quality and data diagnosticity. Decision Support Systems, 120, 38–49. https://doi.org/10.1016/j.dss.2019.03.008.

    Article  Google Scholar 

  • Harter, J.K., Schmidt, F.L., & Hayes, T.L. (2002). Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: a meta-analysis. Journal of Applied Psychology, 87(2), 268.

    Article  Google Scholar 

  • Hom, P.W., Lee, T.W., Shaw, J.D., & Hausknecht, J.P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102(3), 530–545. https://doi.org/10.1037/apl0000103.

    Article  Google Scholar 

  • Hwang, C.L., & Yoon, K. (1981). Multiple attribute decision making. Berlin: Springer. https://doi.org/10.1007/978-3-642-48318-9.

    Book  Google Scholar 

  • Jin, C., De-lin, L., & Fen-xiang, M. (2009). An improved ID3 decision tree algorithm. In 2009 4th International Conference on Computer Science & Education. https://doi.org/10.1109/iccse.2009.5228509: IEEE.

  • Kaggle. (2017). Hr analytics dataset. https://www.kaggle.com/lnvardanyan/hr-analytics#turnover.csv, Online; Stand 04. April 2020.

  • Khodabakhsh, M., Kahani, M., & Bagheri, E. (2018). Predicting future personal life events on twitter via recurrent neural networks. Journal of Intelligent Information Systems. https://doi.org/10.1007/s10844-018-0519-2.

  • Koh, H.C., & Goh, C.T. (1995). An analysis of the factors affecting the turnover intention of non-managerial clerical staff: a Singapore study. The International Journal of Human Resource Management, 6(1), 103–125. https://doi.org/10.1080/09585199500000005.

    Article  Google Scholar 

  • Kohavi, R., & Li, C.H. (1995). Oblivious decision trees graphs and top down pruning. In Proceedings of the 14th International joint conference on artificial intelligence. IJCAI’95, (Vol. 2 pp. 1071–1077). San Francisco: Morgan Kaufmann Publishers Inc.

  • Krylovas, A., Dadelo, S., Kosareva, N., & Zavadskas, E.K. (2017). Entropy–KEMIRA approach for MCDM problem solution in human resources selection task. International Journal of Information Technology &, Decision Making, 16(05), 1183–1209. https://doi.org/10.1142/s0219622017500274.

    Article  Google Scholar 

  • Lu, L., & Yuan, Y. (2018). A novel TOPSIS evaluation scheme for cloud service trustworthiness combining objective and subjective aspects. Journal of Systems and Software, 143, 71–86. https://doi.org/10.1016/j.jss.2018.05.004.

    Article  Google Scholar 

  • Mendoza-Gȯmez, R., Ríos-Mercado, V., & Valenzuela-Ocaña, K.B. (2019). An efficient decision-making approach for the planning of diagnostic services in a segmented healthcare system. International Journal of Information Technology &, Decision Making, 1–35, https://doi.org/10.1142/s0219622019500196.

  • Micci-Barreca, D. (2001). A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems. SIGKDD Explor Newsl, 3(1), 27–32. https://doi.org/10.1145/507533.507538.

    Article  Google Scholar 

  • Morrow, P.C., McElroy, J.C., Laczniak, K.S., & Fenton, J.B. (1999). Using absenteeism and performance to predict employee turnover: Early detection through company records. Journal of Vocational Behavior, 55(3), 358–374. https://doi.org/10.1006/jvbe.1999.1687.

    Article  Google Scholar 

  • Pargent, F., Bischl, B., & Thomas, J. (2019). A benchmark experiment on how to encode categorical features in predictive modeling. Master Thesis in Statistics, Ludwig-Maximilians-Universität München, Leopoldstr 13, 80802 München.

  • Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., & Gulin, A. (2018). Catboost: unbiased boosting with categorical features. In Advances in neural information processing systems (pp. 6638–6648).

  • Punnoose, R., & Ajit, P. (2016). Prediction of employee turnover in organizations using machine learning algorithms. International Journal of Advanced Research in Artificial Intelligence 5(9)https://doi.org/10.14569/ijarai.2016.050904.

  • Rashid, T.A., & Jabar, A.L. (2016). Improvement on predicting employee behaviour through intelligent techniques. IET Networks, 5(5), 136–142. https://doi.org/10.1049/iet-net.2015.0106.

    Article  Google Scholar 

  • Ren, C., & Li, H. (2011). Analysis on human resource management of travel agencies. In 2011 International Conference on Computer Science and Service System (CSSS). https://doi.org/10.1109/csss.2011.5974582: IEEE.

  • Saaty, R. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3-5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8.

    Article  MathSciNet  MATH  Google Scholar 

  • Sanders, R. (1987). The pareto principle: its use and abuse. Journal of Services Marketing, 1(2), 37–40. https://doi.org/10.1108/eb024706.

    Article  Google Scholar 

  • Saradhi, V.V., & Palshikar, G.K. (2011). Employee churn prediction. Expert Systems with Applications, 38(3), 1999–2006. https://doi.org/10.1016/j.eswa.2010.07.134.

    Article  Google Scholar 

  • Sexton, R.S., McMurtrey, S., Michalopoulos, J.O., & Smith, A.M. (2005). Employee turnover: a neural network solution. Computers & Operations Research, 32(10), 2635–2651. https://doi.org/10.1016/j.cor.2004.06.022.

    Article  Google Scholar 

  • Shannon, C.E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3–55.

    Article  MathSciNet  Google Scholar 

  • Sikaroudi, E., Mohammad, A., Ghousi, R., & Sikaroudi, A. (2015). A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing). Journal of Industrial and Systems Engineering, 8(4), 106–121.

    Google Scholar 

  • Tarnowska, K., & Ras, Z. (2018). From knowledge discovery to customer attrition. In Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01851-1_40 (pp. 417–425): Springer International Publishing.

  • Tarnowska, K., Ras, Z.W., & Daniel, L. (2020). Recommender system for improving customer loyalty. Springer International Publishing, https://doi.org/10.1007/978-3-030-13438-9.

  • Taşabat, S.E. (2019). A novel multicriteria decision-making method based on distance, similarity, and correlation: DSC TOPSIS. Mathematical Problems in Engineering, 2019, 1–20. https://doi.org/10.1155/2019/9125754.

    Article  MathSciNet  Google Scholar 

  • Tomar, A., & Jana, P.K. (2018). Mobile charging of wireless sensor networks for internet of things: A multi-attribute decision making approach. In Distributed Computing and Internet Technology. https://doi.org/10.1007/978-3-030-05366-6_26 (pp. 309–324 ): Springer International Publishing.

  • Wang, T.C., & Lee, H.D. (2009). Developing a fuzzy topsis approach based on subjective weights and objective weights. Expert Systems with Applications, 36(5), 8980–8985.

    Article  Google Scholar 

  • Webb, G.I., Sammut, C., Perlich, C., Horváth, T., Wrobel, S., Korb, K.B., Noble, W.S., Leslie, C., Lagoudakis, M.G., Quadrianto, N., Buntine, W.L., Quadrianto, N., Buntine, W.L., Getoor, L., Namata, G., Getoor, L., Xin Jin, J.H., Ting, J.A., Vijayakumar, S., Schaal, S., & Raedt, L.D. (2011). Logistic regression. In Encyclopedia of Machine Learning. https://doi.org/10.1007/978-0-387-30164-8_493 (pp. 631–631). US: Springer.

  • Xiao, J., Zhu, X., Huang, C., Yang, X., Wen, F., & Zhong, M. (2019). A new approach for stock price analysis and prediction based on SSA and SVM. International Journal of Information Technology & Decision Making, 18(01), 287–310. https://doi.org/10.1142/s021962201841002x.

    Article  Google Scholar 

  • Yeh, C.H. (2002). A problem-based selection of multi-attribute decision-making methods. International Transactions in Operational Research, 9(2), 169–181.

    Article  Google Scholar 

  • Yigit, I.O., & Shourabizadeh, H. (2017). An approach for predicting employee churn by using data mining. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). https://doi.org/10.1109/idap.2017.8090324: IEEE.

  • Zhou, M., Liu, X.B., Chen, Y.W., & Yang, J.B. (2018). Evidential reasoning rule for madm with both weights and reliabilities in group decision making. Knowledge-Based Systems, 143, 142–161.

    Article  Google Scholar 

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  1. Department of Computer Science & Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India

    Nishant Jain, Abhinav Tomar & Prasanta K. Jana

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  1. Nishant Jain
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Jain, N., Tomar, A. & Jana, P.K. A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning. J Intell Inf Syst 56, 279–302 (2021). https://doi.org/10.1007/s10844-020-00614-9

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