Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects

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.