A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

中文翻译：

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基于互信息和支持向量机的特征选择方法

为了消除冗余特征，提高分类精度，提出了一种基于互信息和支持向量机（SVM）的特征选择方法。首先，通过互信息计算特征之间的局部相关性和整体相关性。相关性反映了特征之间的信息包含关系，因此通过相关性分析对特征进行评价，剔除冗余特征。随后，定义了平均影响值（MIV）的概念，计算了基于MIV的SVM网络中输入变量对输出变量的影响程度。用 MIV 描述的特征的重要性权重按降序排序。最后，SVM分类器用于根据特征组合的分类精度，以特征的MIV顺序为参考，进行特征选择。用UCI的三个标准数据集进行了仿真实验，结果表明，该方法不仅可以有效降低特征维数，分类准确率高，而且具有良好的鲁棒性。