Feature selection is one of the major aspects of pattern classification systems. In previous studies, Ding and Peng recognized the importance of feature selection and proposed a minimum redundancy feature selection method to minimize redundant features for sequential selection in microarray gene expression data. However, since the minimum redundancy feature selection method is used mainly to measure the dependency between random variables of mutual information, the results cannot be optimal without consideration of global feature selection. Therefore, based on the framework of minimum redundancy-maximum correlation, this paper introduces entropy to measure global feature selection and proposes a new feature subset evaluation method, differential correlation information entropy. In our function, different bivariate correlation metrics are selected. Then, the feature selection is completed through sequence forward search. Two different classification models are used on eleven standard data sets of the UCI machine learning knowledge base to compare various comparison algorithms, such as mRMR, reliefF and feature selection method with joint maximal information entropy, with our method. The experimental results show that feature selection based on our proposed method is obviously superior to that of other models.

特征选择是模式分类系统的主要方面之一。在以前的研究中，丁和彭认识到特征选择的重要性，并提出了一种最小冗余特征选择方法，以最小化用于微阵列基因表达数据中顺序选择的冗余特征。但是，由于最小冗余特征选择方法主要用于测量互信息的随机变量之间的相关性，因此如果不考虑全局特征选择，结果将不是最佳的。因此，在最小冗余-最大相关的框架下，引入熵来度量全局特征选择，并提出了一种新的特征子集评估方法-差分相关信息熵。在我们的函数中，选择了不同的双变量相关度量。然后，通过序列前向搜索完成特征选择。在UCI机器学习知识库的11个标准数据集上使用了两种不同的分类模型，以将各种比较算法（例如mRMR，reliefF和具有联合最大信息熵的特征选择方法）与我们的方法进行比较。实验结果表明，基于我们提出的方法的特征选择明显优于其他模型。