Study of Forecasting Algorithm for Support Vector Machines Based on Rough Sets
Yuancheng Li · Shuju caiji yu chuli · 2003
By analyzing the generalities and specialities of rough sets (RS) and support vector machines (SVM) in knowledge representation and process of classification, a minimum decision network combining RS with SVM in intelligence processing is investigated, and a kind of SVM system on RS is proposed for forceasting. Using RS theory on the advantage of dealing with great data and eliminating redundant information, the system reduces the training data of SVM, and overcomes the disadvantage of great data and slow speed. Finally, the system is used to forecast Shanghai Stock Exchange Index, and experimental results prove that the approach can achieve greater forecasting accuracy and generalization ability than the BP neural network and standard SVM.