Abstract
This paper proposes a new class of data mining problems in which agents replace their current object (predecessor) by another object (replacement or successor); the problem is to discover the knowledge used by the agents in identifying suitable successors. While such replacement data is available in many practical applications, in this paper we explore a problem in HR analytics, viz., replacing person in a key position in a project by another most suitable person from other employees. We propose unsupervised (distance-based) algorithms for finding suitable replacements. We also apply several standard classification techniques. This paper is the first in applying metric learning algorithms to a problem in HR analytics. We compare the approaches using a real-life replacement dataset from a multinational IT company. Results show that metric learning is a promising approach that captures the implicit knowledge for replacement identification.
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
Similar content being viewed by others
References
Archaux, C., Martin, A., Khenchaf, A.: An SVM based churn detector in prepaid mobile telephony. In: Proceedings of International Conference on Information and Communication Technologies: From Theory to Applications (2004)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Hung, S.-Y., Yen, D.C., Wang, H.-Y.: Applying data mining to telecom churn management. Expert Syst. Appl. 31, 515–524 (2006)
Van den Poel, D., Coussement, K.: Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst. Appl. 34(9), 313–327 (2008)
Kulis, B.: Metric learning: a survey. Found. Trends Mach. Learn. 5(4), 287–364 (2012)
Lariviere, B., den Poel, D.V.: Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst. Appl. 29, 472–484 (2005)
Junxiang, L.: Predicting customer churn in the telecommunications industry - an application of survival analysis modeling using sas. In: Proceedings of SAS User Group International, pp. 114–127 (2002)
Morik, K., Köpcke, H.: Analysing customer churn in insurance data – a case study. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 325–336. Springer, Heidelberg (2004)
Mozer, M.C., Wolniewicz, R., Grimes, D.B., Johnson, E., Kaushansky, H.: Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans. Neural Netw. 11(3), 690–696 (2000)
Saradhi, V.V., Palshikar, G.K.: Employee churn prediction. Expert Syst. Appl. 38(3), 1999–2006 (2011)
Wei, C.-P., Chiu, I.-T.: Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst. Appl. 23, 103–112 (2002)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning, with application to clustering with side-information. In: Proceedings of Advances in Neural Information Processing Systems, vol. 15, pp. 505–512 (2003)
Acknowledgments
We thank Dr. Ritu Anand, VP (HR), Preeti Gulati and other HR executives in TCS for enthusiastically supporting this work. Thanks to our team members for discussions and other help.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Palshikar, G.K., Sahu, K., Srivastava, R. (2015). After You, Who? Data Mining for Predicting Replacements. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-26832-3_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26831-6
Online ISBN: 978-3-319-26832-3
eBook Packages: Computer ScienceComputer Science (R0)