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Temporary Staffing Services: A Data Mining Perspective

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2 Author(s)
Jeroen DHaen ; Dept. of Marketing, Ghent Univ., Ghent, Belgium ; Dirk Van Den Poel

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.

Published in:

2012 IEEE 12th International Conference on Data Mining Workshops

Date of Conference:

10-10 Dec. 2012