用于多分类问题的最小二乘支持向量分类—回归机

Least square support vector classification-regression machine for multi-classification problems

基于支持向量机(SVM)的三分类方法是处理多分类问题的一类方法。提出了最小二乘支持向量分类回归机(LSSVCR)算法,通过最小二乘目标函数充分考虑所有样本点对分类的影响,使得训练集中即使有个别样本点被标错类别,对分类结果也不会产生太大的影响,从而提高分类的准确性。该方法能够提高分类的准确率和分类速度,同时算法对于不同类别间样本数目差异较大的情况也有很好的分类效果。数值实验结果表明所提算法是可行的,且与已有的三分类算法相比在分类准确性上平均提高了2.57%,在运算速度上也有了较大的提高。

Tri-class classification method based on Support Vector Machine （SVM） is a kind of method for solving multi-class classification problems. Least Square Support Vector Classification-Regression （LSSVCR） was proposed, which considered the effects of all the sample points by using least squares objective function. Even if there were wrongly marked sample points in the training set, the result would not be affected largely by them. LSSVCR was more accurate and faster, and it was efficient for the problems that there are large differences among the number of sample points in different classes. The numerical experiments show that the proposed method raises the accuracy by 2.57% on average compared to the existing tri-classification methods.