Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
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.
Companies, Decision trees, Data mining, Employment, Predictive models, Industries, Accuracy, Feature selection, temporary staffing, data mining, bagged decision trees
Jeroen DHaen, Dirk Van Den Poel, "Temporary Staffing Services: A Data Mining Perspective", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 287-292, doi:10.1109/ICDMW.2012.103