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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 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.
