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Temporary staffing services: a data mining perspective

Jeroen D'Haen (UGent) and Dirk Van den Poel (UGent)
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Abstract
Research on the temporary staffing industry discusses different topics ranging from workplace safety to the internationalization of temporary labor. However, there is a lack of data mining studies concerning this topic. This paper meets this void and uses a financial dataset as input for the estimated models. Bagged decision trees were utilized to cope with the high dimensionality. Two bagged decision trees were estimated: one using the whole dataset and one using the top 12 predictors. Both had the same predictive performance. This means we can highly reduce the computational complexity, without losing accuracy.
Keywords
LABOR, EMPLOYMENT, INTERNATIONALIZATION, Data mining, Temporary staffing services, Bagged decision trees, Feature selection, ARRANGEMENTS, CONTRACTORS, SELECTION, INDUSTRY, MARKETS, WORKERS, MODEL

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Citation

Please use this url to cite or link to this publication:

Chicago
D’Haen, Jeroen, and Dirk Van den Poel. 2012. “Temporary Staffing Services: a Data Mining Perspective.” In 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012), 287–292. IEEE.
APA
D’Haen, J., & Van den Poel, D. (2012). Temporary staffing services: a data mining perspective. 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012) (pp. 287–292). Presented at the 12th IEEE International Conference on Data Mining (ICDM), IEEE.
Vancouver
1.
D’Haen J, Van den Poel D. Temporary staffing services: a data mining perspective. 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012). IEEE; 2012. p. 287–92.
MLA
D’Haen, Jeroen, and Dirk Van den Poel. “Temporary Staffing Services: a Data Mining Perspective.” 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012). IEEE, 2012. 287–292. Print.
@inproceedings{3151127,
  abstract     = {Research on the temporary staffing industry discusses different topics ranging from workplace safety to the internationalization of temporary labor. However, there is a lack of data mining studies concerning this topic. This paper meets this void and uses a financial dataset as input for the estimated models. Bagged decision trees were utilized to cope with the high dimensionality. Two bagged decision trees were estimated: one using the whole dataset and one using the top 12 predictors. Both had the same predictive performance. This means we can highly reduce the computational complexity, without losing accuracy.},
  author       = {D'Haen, Jeroen and Van den Poel, Dirk},
  booktitle    = {12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012)},
  isbn         = {9781467351645},
  keywords     = {LABOR,EMPLOYMENT,INTERNATIONALIZATION,Data mining,Temporary staffing services,Bagged decision trees,Feature selection,ARRANGEMENTS,CONTRACTORS,SELECTION,INDUSTRY,MARKETS,WORKERS,MODEL},
  language     = {eng},
  location     = {Brussels, Belgium},
  pages        = {287--292},
  publisher    = {IEEE},
  title        = {Temporary staffing services: a data mining perspective},
  url          = {http://dx.doi.org/10.1109/ICDMW.2012.103},
  year         = {2012},
}

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