A Multi-subject Classification Algorithm Based on SVM Geometric interpretation

A new multi-subject classification algorithm based on support vector machine(SVM) is proposed. For each class of training samples, a minimum convex shell surrounding as many samples as possible is constructed in the feature space by using soft SK algorithm, and finally the multi-subject classifier composed of multiple convex shells is obtained. For the sample to be classified, its classes are determined according to the convex hulls in which it are located. If it is not in any convex hull, firstly, the membership degree is determined by the distance that from it to the centroid of each class sample, and then its class to which it belongs is determined according to the membership degree. The classification experiments are carried out on the standard dataset Reuters 21578, and the classification performance is compared with the hyperellipsoid SVM classification algorithm. The experimental results show that compared with the hyperellipsoid SVM classification algorithm, the proposed algorithm can ensure the inheritability of the classifier and the classification accuracy is significantly improved, which effectively solves the influence of sample distribution shape on classification performance.