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Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects 被引量:4

Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects
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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. 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.
出处 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2014年第2期174-180,共7页
基金 Item Sponsored by National Natural Science Foundation of China(61050006)
关键词 multi-class classification least squares twin support vector machine error variable contribution WEIGHT binary tree strip steel surface multi-class classification least squares twin support vector machine error variable contribution weight binary tree strip steel surface
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