Measurement of attribute classification power based on derivative information

Attribute importance ranking still is a key problem required to be solved for the classification problem. The lack of efficient heuristic information is the fundamental reason that affects the attribute selection in data mining. In this paper, to determining the importance level of the attributes, a new measure based on partial derivative distribution of the classification hypersurface output corresponding to the input attributes is proposed. This paper indicates that if the classification hypersurface is acquired by SVM, it is more convenient to measure the attribute importance ranking. The validity of the proposed method is experimentally evaluated, the experimental results prove that the approach is more efficient.