A New SVM Multi-Class Classification Algorithm Based on Sample Scale and Distribution Area
Yu Ping Qin, Peng Da Qin, Shu Xian Lun, Yi Wang · Advanced materials research · 2013
A new SVM multi-class classification algorithm is proposed. Firstly, the optimal binary tree is constructed by the scale and the distribution area of every class sample, and then the sub-classifiers are trained for every non-leaf node in the binary tree. For the sample to be classified, the classification is done from the root node until someone leaf node, and the corresponding class of the leaf node is the class of the sample. The experimental results show that the algorithm improves the classification precision and classification speed, especially in the situation that the sample scale is less but its distribution area is bigger, the algorithm can improve greatly the classification performance.