The Strip Steel Surface Defect Recognition Based on Multiple Support Vector Hyper-Sphere with Feature and Sample Weights

摘要

For strip steel surface defect dataset with abnormal samples and weakly relevant features, a novel multi-class classification method is realized in this paper. Firstly, weight parameter representing the importance degree of sample is obtained by calculating the clustering information of samples in the training dataset. Then, weight parameter representing the importance degree of feature is obtained by utilizing sparse important samples dataset and Relief-F algorithm. After that, a multiple support vector hyper-sphere with feature and sample weights (FSW-MSVH) is proposed. The novel classifier can realize multi-class classification. Finally, the multi-class classifier is used to do classification experiments for eight classes of strip steel surface defect. Experimental results show that FSW-MSVH classifier can greatly improve classification accuracy and speed.