In this paper, a novel feature selection method based on rough sets and mutual information is proposed. The dependency of each feature guides the selection, and mutual information is employed to reduce the features which do not favor addition of dependency significantly. So the dependency of the subset found by our method reaches maximum with small number of features. Since our method evaluates both definitive relevance and uncertain relevance by a combined selection criterion of dependency and class-based distance metric, the feature subset is more relevant than other rough sets based methods. As a result, the subset is near optimal solution. In order to verify the contribution, eight different classification applications are employed. Our method is also employed on a real Alzheimer’s disease dataset, and finds a feature subset where classification accuracy arrives at 81.3 %. Those present results verify the contribution of our method.

中文翻译：

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一种新的特征选择方法及其应用

本文提出了一种新的基于粗糙集和互信息的特征选择方法。每个特征的依赖关系指导选择，并利用互信息来减少不利于显着增加依赖的特征。因此，我们的方法找到的子集的依赖性在特征数量较少时达到最大值。由于我们的方法通过依赖项和基于类的距离度量的组合选择标准来评估确定的相关性和不确定的相关性，因此特征子集比其他基于粗糙集的方法更相关。因此，该子集接近最优解。为了验证贡献，使用了八种不同的分类应用程序。我们的方法也用于真实的阿尔茨海默病数据集，并找到分类准确率达到 81.3% 的特征子集。这些目前的结果验证了我们方法的贡献。