A Hybrid Classification Algorithm Based on Rough Sets and Support Vector Machines

In this paper we present a novel hybrid algorithm based on attribute reduction of RS and classification principles of SVM. Firstly, the attribute reduction of RS has been applied as preprocessor so that we can delete redundant attributes and conflicting objects from decision making table but remain efficient information lossless. Then, we realize classification modeling and forecasting test based on SVM. By this method, we can greatly reduce the dimension of data, highly decrease the complexity in the process of SVM classification cut down the occupied memory, and prevent the over-fit of training model at a certain extent, but obtain the good classification performance. Finally, the simulation experiments show the effectiveness of the suggested hybrid method.