A Machine Learning Application for Human Resource Data Mining Problem

Abstract

Apply machine learning methods to data mining domain can be more helpful to extract useful knowledge for problems with changing conditions. Human resource allocation is a kind of problem in data mining domain. It presents machine learning techniques to dissolve it. First, we construct a new model which optimizes the multi-objectives allocation problem by using fuzzy logic strategy. One of the most important problems in the model is how to get the precise individual capability matrixes. Machine learning method by being told is well used to settle the problem in this paper. In the model, appraisal values about employees are saved in knowledge warehouse. Before tasks allocation, machine learning approach provides the capability matrixes based on the existing data sets. Then Task-Arrange or Hungarian Algorithm provides the final solution with our proposed matrixes. After present tasks are finished, machine learning method by being told can update the matrixes according to the suggestions on employees’ performance provided by the specialists. Useful knowledge can be well mined in cycles by learning approach. As a numerical example demonstrated, it is helpful to make a realistic decision on human resource allocation under a dynamic environment for organizations.

National 973 Project in China (Grant No. 2004CB719401).