Combining Rough Sets, Support Vector Machines And Applications

A new learning machine---support vector machine(SVM) is one of the statistical learning theories,which also based on the statistical learning theory.Now,SVM is viewed as the most convincing tool in solving the classification and regression problems and the research focus in the filed of machine learning after neural network.On the basis of structural risk minimization and VC dimension,it seeking the optimum tradeoff between model complexities and learning abilities under the limited samples,to achieve the best generalization.SVM is regard as a good development to traditional classifier,showed many unique advantages in solving small samples,nonlinear,high dimension and other machine learning problems.It is well known,SVM has the advantage of global convergence,but the process become more complexity when it treat multi-class problems,also the computation cost and training time increased.Therefore,an improved algorithm based on neighborhood theory is proposed to handle the problems above.The rough set theory is also introduced to reduce data's features,so the time complexity is decreased.Actual results proved our method's validity.In this paper our main work is as follows:First,SVM is a two-class classifier previously,it need transformation when we meet a multi-class problem.The traditional method of transforming a multi-class problem is reconstructing the classes and the outputs of SVM classifier,and then the recognition rate is enhanced.Based on this method,we proposed an improved algorithm which changes the distribution of new samples by using mapping,after that,the same samples are more compact and the different samples are looser,which will contribute to classification.At last,with-class scatter and between-class scatter are calculated for validation in UCI database.Second,combining RS with SVM,use rough set theory to reduce data's features, delete the irrelevant features according to the equivalence relation,then,the time complexity is decreased.At last,the attribute reduction and neighborhood theory are combined with support vector regression;this improved algorithm is applied to electric power system load forecast and compared with traditional method to prove its advantages.