Abstract
We propose a new method for building a classifier ensemble, based on subgroup discovery techniques in data mining. We apply subgroup discovery techniques to a labeled training dataset to discover interesting subsets, characterized by a conjuctive logical expression (rule), where such subset has an unusually high dominance of one class. Treating these rules as base classifiers, we propose several simple ensemble methods to construct a single classifier. Another novel aspect of the paper is that it applies these ensemble methods, along with standard anomaly detection and classification, to automatically identify high potential (HIPO) employees - an important problem in management. HIPO employees are critical for future-proofing the organization in the face of attrition, economic uncertainties and business challenges. Current HR processes for HIPO identification are manual and suffer from subjectivity, bias and disagreements. Proposed data-driven analytics algorithms address some of these issues. We show that the new ensemble methods perform better than other methods, including other ensemble methods on a real-life case-study dataset of a large multinational IT services company.
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The authors thank Dr. Ritu Anand, Preeti Gulati for their support and our team members for much help.
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Palshikar, G.K., Sahu, K., Srivastava, R. (2016). Ensembles of Interesting Subgroups for Discovering High Potential Employees. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_17
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DOI: https://doi.org/10.1007/978-3-319-31750-2_17
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