Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LS-SVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LS-SVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability.

最小二乘支持向量机（LS-SVM）的高速性在钢材表面缺陷分类中起着重要作用。但是，从实际生产线获得的缺陷样本可能是噪音。当存在噪声样本时，LS-SVM在分类阶段的分类性能较差。因此，在分类阶段，有必要设计一种有效的算法来处理从实际生产线获得的缺陷数据集。为此，采用自适应加权函数来减少噪声样本的不利影响。此外，尽管LS-SVM具有较快的速度，但如果训练样本的数量很大，它仍然会遭受较高的计算复杂度。钢表面缺陷分类的时间应尽可能短。因此，采用稀疏策略来修剪训练样本。最后，由于钢表面缺陷分类属于不平衡数据分类，因此LS-SVM算法不适用。因此，引入不平衡数据信息以提高分类性能。综合考虑上述因素，提出了一种改进的LS-SVM分类模型，称为ILS-SVM。实验结果表明，该算法具有速度快，抗噪能力强的优点。提出了一种改进的LS-SVM分类模型，称为ILS-SVM。实验结果表明，该算法具有速度快，抗噪能力强的优点。提出了一种改进的LS-SVM分类模型，称为ILS-SVM。实验结果表明，该算法具有速度快，抗噪能力强的优点。