Classification Method of SVM Based on Lower Approximations Theory of Rough Set
Hong Cheng-yu · Journal of Qufu Normal University · 2008
The traditional rough set theory can not deal with the continuous attribute,and the classification rules which are obtained from the rough set are mostly complicated.Though SVM can get the concise classification rules and can deal with the continuous attribute,but it can only be used for small samples.This paper presents a SVM classification method based on rough set's lower approximations theory and it's application in continuous attribute.Experiments results show that the method can preserve the necessary information needed by SVM,improve the prediction accuracy and reduce the training time of support vector machine.