Dual membership SVM method based on spectral clustering

A new fuzzy support vector machine algorithm with dual membership values based on spectral clustering method is proposed to overcome the shortcoming of the normal support vector machine algorithm, which divides the training datasets into two absolutely exclusive classes in the binary classification, ignoring the possibility of “overlapping” region between the two training classes. The proposed method handles sample “overlap” efficiently with spectral clustering, overcoming the disadvantages of over-fitting well, and improving the data mining efficiency greatly. Simulation provides clear evidences to the new method.