ISIJ International
Online ISSN : 1347-5460
Print ISSN : 0915-1559
ISSN-L : 0915-1559
Regular Article
Strip Steel Surface Defect Classification Method Based on Enhanced Twin Support Vector Machine
Maoxiang ChuRongfen GongAnna Wang
Author information
JOURNALS OPEN ACCESS FULL-TEXT HTML

2014 Volume 54 Issue 1 Pages 119-124

Details
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 multi-class 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.

Information related to the author
© 2014 by The Iron and Steel Institute of Japan
Previous article Next article
feedback
Top