Strip Steel Surface Defect Classification Method Based on Enhanced Twin Support Vector Machine

Abstract

The strip steel surface defect classification belongs to multi-class classification. It demands high classification accuracy and efficiency. However, traditional methods are not fit for abnormal datasets, such as the large-scale, sparse, unbalanced and corrupted dataset. So a novel classification method is proposed in this paper based on enhanced twin support vector machine (TWSVM) and binary tree. According to the density information, the large-scale dataset is pruned, the sparse dataset is added with unlabeled samples, and TWSVM is improved to multi-density TWSVM (MDTWSVM) which has efficient successive overrelaxation (SOR) algorithm. Finally, MDTWSVM and binary tree are combined together to realize multiclass classification. Some experiments are done on the strip steel surface defect datasets with the proposed algorithm. Experimental results show that MDTWSVM has higher accuracy and efficiency than the other methods of multi-class classification for the strip steel surface defect.