Application Of Rough Set And SVM In Discretization Of Continuous Attribute

Rough set and the support vector machine (SVM) are the data mining methods which are aimed to make the fixed model. Rough set theory is suitable for the magnanimous data, simple and easy to use. SVM is a classification method proposed in the statistical learning theory, its structural risk minimization and the kernel function theory have avoided the traditional method shortcoming of "dimension disaster" and "over-fitting" and so on.By unifying rough set and SVM, this article proposed a method: first, used the rough set to carry on the pre-treatment, and then, used the support vector machine to make the precise classification.This article first introduced rough set and support vector machine elementary theory, made a brief review at lower approximation, upper approximation, decision rule of rough set, as well as structural risk minimization, kernel function theory of support vector machine, and analyzed two methods advantage and limitation in the data mining domain.Then, in view of the previous discrete method probably lose the massive information and the classification rule it obtained is complex and not easy to be understood, chapter five proposed a method that based on the lower approximation of rough set. This method can make the pre-treatment to the mass data and get the classification that definitely belong to some category according to the lower approximation of rough set, and delete some possible noise data. This method will obtain some decision rule finally. This method will not destroy the indiscernibility relation of original data, moreover the classified rule is brief.After that, using the support vector machine can make the precisely classify only with the support vector related, presents a SVM classification method based on rough sets lower approximations theory and its application in continuous attribute.Finally, experiment results show that the method can preserve the necessary information needed by SVM, and can improve the prediction accuracy and reduce the training time of support vector machine.