基于SMC策略的线性不可分数据集的分类算法OA

For the classification problem of linearly inseparable datasets,a support vector machine(SVM)kernel function parameter optimization algorithm was proposed based on the sliding mode control(SMC)strategy by apply-ing the SMC idea to the SVM kernel function parameter optimization process.By designing the error equation and sliding surface,the association between SVM classification objective function and SMC was established,and then the iteration update rules of kernel function parameters and cost function was derived.The algorithm improved clas-sification performance while reducing the number of support vectors by dynamically adjusting the kernel parameters of SVM.In the experiment part,six UCI datasets,such as Iris and Heart disease,were used to verify the validity of the algorithm.The results showed that compared with traditional SVM,the proposed algorithm reduced the num-ber of support vectors by 56.25%on the Iris dataset,and the test accuracy remained at 100%.Test accuracy in-creased by 13.58 percentage points on the Heart disease dataset.Furthermore,the proposed algorithm,compared with existing optimization algorithms,showed a higher classification accuracy on some datasets.