A decision-making approach to personnel selection based on rough set and support vector machine

In this paper, an approach to personnel selection that integrates multi-attribute decision making techniques from a machine learning perspective is described. The approach is based on rough set theory and support vector machine, which is derived from statistical learning theory. Statistical learning theory is designed to solve small sample learning problems, so the approach is suitable for small and medium-sized businesses. Firstly, an attribute reduction algorithm based on rough set is adopted to find out key attributes which affect employee performance. Then a support vector machine classifier that supports decision making for personnel selection is built with key attributes. The experiment results show that the new approach obtains a better accuracy than some existing ones in small sample situation.