Abstract
Employee absenteeism is a substantial problem faced by many organizations. It severely affects the productive operations in organizations. In the recent years, predictive modeling using ensemble learning techniques has increased the attention of researchers to develop competent models for various predictive tasks. Ensemble techniques combine the predictions of multiple models to yield a single consolidated decision. Thus, predictive modeling with the help of ensemble learning techniques can predict employee absenteeism so that the human resource department can devise intervention policies and lessen monetary losses due to absenteeism. Therefore, in this direction, this study develops models with the help of boosting-based ensemble learning aggregate with data balancing to predict employee absenteeism. The predictive accuracy of the absenteeism prediction models is evaluated using strong performance measures; area under receiver operator characteristics curve, balance and geometric mean. The results are also examined statistically with statistical analysis. The boosting-based ensemble learning techniques are effective for predicting employee absenteeism according to the results of the study.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali Shah SA, Uddin I, Aziz F, Ahmad S, Al-Khasawneh MA, Sharaf M (2020) An enhanced deep neural network for predicting workplace absenteeism. Complexity 2020
Oza NC, Tumer K (2008) Classifier ensembles: select real-world applications. Inf Fusion 9(1):4–20
Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2011) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(4):463–484
Randhawa K, Loo CK, Seera M, Lim CP, Nandi AK (2018) Credit card fraud detection using AdaBoost and majority voting. IEEE Access 6:14277–14284
Patil S, Nemade V, Soni PK (2018) Predictive modelling for credit card fraud detection using data analytics. Procedia Comput Sci 132:385–395
Alsaeedi A, Khan MZ (2019) Software defect prediction using supervised machine learning and ensemble techniques: a comparative study. J Softw Eng Appl 12(5):85–100
Gao J, Zhou Z, Ai J, Xia B, Coggeshall S (2019) Predicting credit card transaction fraud using machine learning algorithms. J Intell Learn Syst Appl 11(3):33–63
de Oliveira EL, Torres JM, Moreira RS, de Lima RAF (2019) Absenteeism prediction in call center using machine learning algorithms. In: World conference on information systems and technologies, Apr 2019. Springer, Cham, pp 958–968
Tewari K, Vandita S, Jain S (2020) Predictive analysis of absenteeism in MNCs using machine learning algorithm. In: Proceedings of ICRIC 2019. Springer, Cham, pp 3–14
Rista A, Ajdari J, Zenuni X (2020) Predicting and analyzing absenteeism at workplace using machine learning algorithms. In: 2020 43rd international convention on information, communication and electronic technology (MIPRO). IEEE, pp 485–490
Lawrance N, Petrides G, Guerry MA (2021) Predicting employee absenteeism for cost effective interventions. Decis Support Syst 113539
Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. In: European conference on principles of data mining and knowledge discovery, Sept 2003. Springer, Berlin, Heidelberg, pp 107–119
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
Hu S, Liang Y, Ma L, He Y (2009) MSMOTE: improving classification performance when training data is imbalanced. In: 2009 second international workshop on computer science and engineering, Oct 2009, vol 2. IEEE, pp 13–17
Guo H, Viktor HL (2004) Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach. ACM SIGKDD Explor Newsl 6(1):30–39
Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2009) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern Part A Syst Hum 40(1):185–197
Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl 6(1):20–29
Galar M, Fernández A, Barrenechea E, Herrera F (2013) EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recogn 46(12):3460–3471
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lata, K. (2023). On the Efficacy of Boosting-Based Ensemble Learning Techniques for Predicting Employee Absenteeism. In: Shukla, A., Murthy, B.K., Hasteer, N., Van Belle, JP. (eds) Computational Intelligence. Lecture Notes in Electrical Engineering, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-19-7346-8_16
Download citation
DOI: https://doi.org/10.1007/978-981-19-7346-8_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7345-1
Online ISBN: 978-981-19-7346-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)