Multi-class classification method using twin support vector machines with multi-information for steel surface defects

Focus on the efficiency and accuracy of multi-class classification for steel surface defects, we propose a new twin support vector machines with multi-information (MTSVMs). The new MTSVMs model is based on binary twin support vector machines. It infuses three kinds of information: boundary samples information, representative samples information and feature weight information. Boundary samples information describes the distribution of samples in boundary region for defect dataset. Representative samples information provides important samples in global and local distribution. They make MTSVMs classifier have perfect execution efficiency and anti-noise performance. Feature weight information excavates strongly relevant features, which improves the accuracy of classifier. For six types of steel surface defect, the MTSVMs model is extended as a multi-class classifier. Experimental results show that our proposed multi-information algorithms have satisfactory performance. Moreover, the final comparative experiments prove that our MTSVMs model has perfect performance in efficiency and accuracy, especially for corrupted defect dataset.