Abstract: Support vector machine is a kind of machine learning algorithm based on statistical learning theory,which has a desirable modeling performance for nonlinear and high-dimensional data,even in the case of small samples.Typically,the data with multiple features would be normalized due to different dimensions.However,it ignores the dissimilarity of different features.A weighted least squares support vector machine was proposed.According to the entropy evaluation method,the feature weights may be determined so that the data could be normalized and weighted.Then the system model would be established through the least squares support vector machine.The experimental results demonstrate the effectiveness and superiority of the proposed method for the system with multiple features.

Key words: Support vector machine,Entropy evaluation method,Multiple features,Feature weight