| Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects简 |
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| 引用本文： | Mao-xiang CHU,An-na WANG,Rong-fen GONG,Mo SHA. Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects简[J]. 钢铁研究学报(英文版), 2014, 21(2): 174-180. DOI: 10.1016/S1006-706X(14)60027-3 |
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| 作者姓名： | Mao-xiang CHU An-na WANG Rong-fen GONG Mo SHA |
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| 基金项目： | Item Sponsored by National Natural Science Foundation of China （61050006） |
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| 摘 要： | 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 classifierr 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 sam- ples and have better classification accuracy. Finally, multi-class classification algorithms of ELS-TWSVM were pro- posed by combining ELS-TWSVM and complete binary tree. Some experiments were made on two-dimensional data- sets 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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| 关 键 词： | 带钢表面缺陷 分类方法 增强型 完全二叉树 数据样本 抑制噪声 支持向量机 数据集 |
| 收稿时间： | 2012-09-11 |
Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects |
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| Mao-xiang CHU,An-na WANG,Rong-fen GONG,Mo SHA. Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects[J]. Journal of Iron and Steel Research, 2014, 21(2): 174-180. DOI: 10.1016/S1006-706X(14)60027-3 |
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| Authors: | Mao-xiang CHU An-na WANG Rong-fen GONG Mo SHA |
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| Affiliation: | 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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| Keywords: | multi-class classification least squares twin support vector machine error variable contribution weight binary tree strip steel surface |
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