Method of multi-classification by improved binary tree based on SVM for welding defects recongnition

To further recognition accuracy,the multi-classification by improved binary tree based on SVM is raised for welding defects recognition.In the welding defects classification,each class separation is computed,the classes of the two smallest class separation are trained to generate the sub-classification SVM_1 and then are combined into a new cluster G.The new cluster G and the remaining classes are computed similarly,and the second sub-classification SVM_2 and new merged cluster H are produced.This work would be repeated until the(k-1)-th sub-classification SVM is obtained and finally a balanced binary tree is come into being.Then,the optimized binary tree based on SVM by clustering is applied in welding defects recognition.The experiments show high recognition accuracy and strong generalization ability by our new algorithm.