Data Dimensionality Reduction Based on Derivative Characteristics of Trained Support Vector Regression

Data dimensionality reduction(DDR) is an important preprocessing technique for data mining, pattern classification and so on. DDR aims at obtaining compact representation of the original data while reduce unimportant or irrelevant data. In this paper we propose a new measure for determining the importance level of the attributes based on the trained support vector regression (SVR) and its derivative characteristics. Based on this new measure, a new approach for data dimensionality reduction based on support vector regression is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve efficiency and effectiveness significantly compared with other data dimensionality reduction approaches.