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A differential evolution based feature selection approach using an improved filter criterion

机译：使用改进的滤波准则的基于差分进化的特征选择方法

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In filter feature selection, mutual information approaches have recently gained a high popularity among researchers. In these approaches, mutual information is commonly used to measure two components: the mutual relevance between each feature and the class labels, and the mutual redundancy between each pair of features. Despite their popularity, it has been pointed in the literature that such feature selection approaches may not fairly estimate the redundancy in high dimensional problems. To alleviate this problem, this paper proposes a new criterion, which uses the concepts of ReliefF instead of the mutual redundancy. Using the proposed criterion, a new differential evolution based filter feature selection approach is developed. The performance comparisons and analysis are conducted by comparing it with the most well-known mutual information feature selection (MIFS) criterion based on maximum-relevance and minimum-redundancy on the differential evolution framework. The results show that performing feature selection using the proposed criterion can generally achieve better classification performance and smaller feature subset size.
机译：在过滤器特征选择中，互信息方法最近在研究人员中广受欢迎。在这些方法中，互信息通常用于衡量两个组成部分：每个要素与类标签之间的相互关联性，以及每对要素之间的相互冗余。尽管它们很流行，但是在文献中已经指出，这样的特征选择方法可能不能公平地估计高维问题中的冗余度。为了缓解这个问题，本文提出了一种新的准则，该准则使用ReliefF的概念代替了相互冗余。使用提出的标准，开发了一种新的基于差分进化的滤波器特征选择方法。性能比较和分析是通过将其与基于差分进化框架上基于最大相关性和最小冗余的最著名的互信息特征选择（MIFS）标准进行比较而进行的。结果表明，使用所提出的准则执行特征选择通常可以实现更好的分类性能和更小的特征子集大小。

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