Denoising method of non-spherical distributed data set
Hongliang Zheng · Journal of Computer Applications · 2011
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.