Steel surface defect classification using multiple hyper-spheres support vector machine with additional information

A novel multiple hyper-spheres support vector machine with additional information (MHSVM+) is proposed for multi-class steel surface defects classification. Originated from binary twin hyper-spheres support vector machine, MHSVM+ uses hyper-sphere to solve classification decision problem. Differently, MHSVM+ is a multi-class classifier, where it builds a corresponding hyper-sphere for each type of defect dataset. Moreover, MHSVM+ introduces learning paradigm using additional information, which means it can learn additional information hidden in defect dataset. Two types of additional information are provided: local neighbor information and local density information. Local neighbor information contains local classification results for defect samples. And local density information is used to capture label noise, isolated samples and important samples in defect dataset. The above two types of additional information are introduced into MHSVM+ model. Finally, MHSVM+ classifier is used to classify six types of steel surface defects. Experimental results show that the novel multi-class classifier has perfect classification accuracy for defect dataset, especially corrupted defect dataset.