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| Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects |
| Mao-xiang CHU1,2,An-na WANG1,Rong-fen GONG1,2,Mo SHA1 |
1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
2. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, Liaoning, China |
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Abstract Considering strip steel surface defect samples, a multi-class classification method was proposed based on enhanced least squares twin support vector machines (ELS-TWSVMs) and binary tree. Firstly, pruning region samples center method with adjustable pruning scale was used to prune data samples. This method could reduce classifier��s training time and testing time. Secondly, ELS-TWSVM was proposed to classify the data samples. By introducing error variable contribution parameter and weight parameter, ELS-TWSVM could restrain the impact of noise samples and have better classification accuracy. Finally, multi-class classification algorithms of ELS-TWSVM were proposed by combining ELS-TWSVM and complete binary tree. Some experiments were made on two-dimensional datasets and strip steel surface defect datasets. The experiments showed that the multi-class classification methods of ELS-TWSVM had higher classification speed and accuracy for the datasets with large-scale, unbalanced and noise samples.
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Received: 12 September 2012
Published: 20 February 2014
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| Fund:Research on the Novel Manifold Learning and Semi-supervised SVM Algorithm for Fault Diagnosis of Complex Industry Process |
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Corresponding Authors:
Mao-Xiang CHU
E-mail: chu52_2004@163.com
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2014, Vol. 21 
