关键词: 支持向量机, 粗糙支持向量机, 噪声过滤系统, 等价类, 去噪

Abstract: Considering the over-sensitiveness of traditional Support Vector Machine (SVM) to noises, and the excessive dependence on the geometric shape of sample set of Fuzzy SVM (FSVM), Rough Support Vector Machines based on Noise Filtering System (NFS-RSVM) was proposed. Firstly, the sample that was most likely to be noise was filtered out by Noise Filtering System (NFS); then the equivalence class which was implied in the data was integrated into the SVM model as a double punishment factor for distinguishing valid and noise samples. The simulation results on UCI show that NFS-RSVM can remove most of the noises effectively, and the accuracy is improved partly compared with the traditional SVM and FSVM. Therefore, NFS-RSVM shows better noise immunity, classification performance and generalization ability when dealing with the non-spherical distributed data set with too many noises.

Key words: Support Vector Machine (SVM), Rough SVM (RSVM), Noise Filtering System (NFS), equivalence class, denoising