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Ensembles of Interesting Subgroups for Discovering High Potential Employees

Published on Apr 19, 2016 in KDD (Knowledge Discovery and Data Mining)
· DOI :10.1007/978-3-319-31750-2_17
Girish Keshav Palshikar9
Estimated H-index: 9
(Tata Consultancy Services),
Kuleshwar Sahu1
Estimated H-index: 1
(Tata Consultancy Services),
Rajiv Srivastava3
Estimated H-index: 3
(Tata Consultancy Services)
Sources
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.
  • References (26)
  • Citations (2)
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References26
Newest
#1Jolyn Gelens (Vrije Universiteit Brussel)H-Index: 4
#2Joeri Hofmans (Vrije Universiteit Brussel)H-Index: 23
Last. Roland Pepermans (Vrije Universiteit Brussel)H-Index: 31
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We examined how perceived distributive and procedural justice affected the relationship between an employee's identification as a high potential (drawn from archival data), job satisfaction and work effort. A questionnaire was distributed within one large company among employees who were and employees who were not identified as a high potential (n = 203). The results indicated that perceptions of distributive justice were significantly higher for employees identified as a high potential. Moreove...
54 CitationsSource
#1Igor Kotlyar (UOIT: University of Ontario Institute of Technology)H-Index: 5
#2Leonard Karakowsky (York University)H-Index: 16
Last. Janet A. Boekhorst (York University)H-Index: 6
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Purpose – The purpose of this paper is to empirically examine how status-based labels, based on future capabilities, can impact people's risk tolerance in decision making. Design/methodology/approach – In this paper the authors developed and tested theoretical arguments using a set of three studies employing a scenario-based approach and a total of 449 undergraduate business students. Findings – The findings suggest that labeling people in terms of future capabilities can trigger perceptions of ...
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#1Zhi-HuaZhouH-Index: 84
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts...
879 Citations
#1Nicky Dries (Vrije Universiteit Brussel)H-Index: 24
#2Roland Pepermans (Vrije Universiteit Brussel)H-Index: 31
Purpose – This paper aims to demonstrate the utility of using some indication of emotional intelligence (EI) to identify high potential in managers. Presupposed correspondences between the EI Personal Factors Model (Bar‐On) and Briscoe and Hall's metacompetency model of continuous learning are elucidated.Design/methodology/approach – The study sample consisted of 51 high potentials and 51 “regular” managers, matched onto one another by managerial level, gender and age. All participants completed...
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#1Rebecca Slan-Jerusalim (U of G: University of Guelph)H-Index: 2
#2Peter A. Hausdorf (U of G: University of Guelph)H-Index: 12
Purpose – The purpose of the present study was to describe the high potential identification practices of Canadian organizations and to assess elements of these practices as they relate to managers' perceptions of organizational justice.Design/methodology/approach – The study reviewed the literature on high potential identification practices and organizational justice to develop a survey for managers attending a leadership conference. Distributive and procedural justice was regressed against the...
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#1Martin AtzmullerH-Index: 1
Subgroup mining is a powerful and broadly applicable data mining approach: In general, the goal is to efficiently discover novel, potentially useful and ultimately interesting knowledge given by subgroup patterns. However, in real-world situations these requirements often cannot be fulfilled, e.g., if the applied methods do not scale for large data sets, if too many results are presented, or if many of the discovered patterns are already known to the user. This work proposes a combination of sev...
16 Citations
#1Martin Atzmueller (University of Würzburg)H-Index: 25
#2Frank Puppe (University of Würzburg)H-Index: 22
In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or sampling-based methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algori...
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#1Branko KavšekH-Index: 5
#2Nada LavračH-Index: 43
Last. Viktor JovanoskiH-Index: 3
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This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. This was achieved by building a classification rule learner APRIORI-C, enhanced with a novel post-processing mechanism, a new quality measure for induced rules (weighted relative accuracy) and using probabilistic classification of instances. Results of APRIORI-SD are similar to the subgroup discovery algorithm CN2-SD while experimental comparisons with CN2, RIPPER...
160 CitationsSource
Aug 21, 2005 in KDD (Knowledge Discovery and Data Mining)
#1Martin ScholzH-Index: 7
Subgroup discovery is a learning task that aims at finding interesting rules from classified examples. The search is guided by a utility function, trading off the coverage of rules against their statistical unusualness. One shortcoming of existing approaches is that they do not incorporate prior knowledge. To this end a novel generic sampling strategy is proposed. It allows to turn pattern mining into an iterative process. In each iteration the focus of subgroup discovery lies on those patterns ...
20 CitationsSource
This paper investigates how to adapt standard classification rule learning approaches to subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures. Experimental ...
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#1Girish Keshav Palshikar (Tata Consultancy Services)H-Index: 9
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The notion of roles is crucial in project management across various domains. A role indicates a broad set of tasks, activities, deliverables and responsibilities that the person needs to carry out within a project. Assigning roles to team members clarifies the expectations of work items to be delivered by each and structures the interactions of the team among themselves as well as with external stakeholders. This paper analyzes a sizeable real-life dataset regarding the actual usage of roles in ...
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