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Feature Selection Method Based on Differential Correlation Information Entropy

机译：基于差分相关信息熵的特征选择方法

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摘要

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.
机译：特征选择是模式分类系统的主要方面之一。在先前的研究中，丁和彭认识到特征选择的重要性，并提出了最小冗余特征选择方法，以最小化微阵列基因表达数据中顺序选择的冗余特征。但是，由于使用最小冗余特征选择方法主要用于测量互动变量之间的依赖性，因此在不考虑全局特征选择的情况下不能最佳。因此，基于最小冗余最大相关的框架，本文介绍了熵测量全局特征选择，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关联度量。然后，通过序列前进搜索完成特征选择。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，以比较各种比较算法，例如MRMR，Relieff和具有关节最大信息熵的MRMR，Relieff和特征选择方法，具有我们的方法。实验结果表明，基于我们所提出的方法的特征选择显然优于其他模型。

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