Modified Support Vector Regression in outlier detection

In order to construct approximation functions on real-life data, it is necessary to remove outliers from the measured raw data before modeling. Although the standard Support Vector Regression based outlier detection methods for non-linear function with multidimensional input have achieved good performance, they have practical issues in computational costs and parameter adjustment. In this paper we propose a practical approach to outlier detection using modified SVR, which reduces computational cost and defines outlier threshold appropriately. We apply this method to both test and industrial data sets for validation.