基于最大间隔的支持向量机特征选取算法研究

支持向量机(Support Vector Machine,简称SVM)是一种有效分类方法.不同特征选取算法对分类器影响不同,结合支持向量机特点,提出了一种基于最大间隔的支持向量机特征选取算法.利用该算法,对Iris测试数据集进行了特征选取并仿真,实验结果表明,该算法不但能够有效去除噪音数据,而且提高了分类器推广与泛化能力.

RESEARCH ON FEATURE SELECTION ALGORITHM BASED ON THE LARGEST MARGIN OF SUPPORT VECTOR MACHINE

Support Vector Machine (SVM) is an effective classification method. Because different feature selection algorithm on the impact of classifier is not the same, combined with the characteristics of SVM , an feature selection algorithm based on the largest margin of SVM is presented. Taking advantage of the new feature selection algorithm to test feature selection and simulation on the Iris data set, the experiment results showed that the new feature selection algorithm can not only remove the noise data effectively, but also enhance the classifier's generalization ability.