A Study on Integrated Evaluating Kernel Classification Performance Using Statistical Methods

This paper explores the research on evaluating kernel classification performance using statistical methods.By employing the corrected resamplet-test and other two statistical methods—k-fold cross-validation and paired t-test,this paper compares their classification abilities on nine normally used kernels.In addition,a new quantitative criterion of evaluating kernel classification performance based on information gain is proposed,which is proved to be the nonlinear function of traditional criteria.Benchmark tests show that there is difference among different criteria,but by using statistical methods some regulations can be turned up among them.Simultaneously,there is great difference among different statistical methods,which affects the evaluating results more than the difference among different criteria does.So only with the integrated methods and criteria the classification performance of different kernels can be evaluated objectively.