A novel feature selection method and its application.
A novel feature selection method and its application.
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DOI：
10.1007/s10844-013-0243-x
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发表时间：
2013-10-01
影响因子：
3.4
通讯作者：
Huang, Di
中科院分区：
文献类型：
--
作者：
Li, Bing;Chow, Tommy W. S.;Huang, Di
提出了一种基于粗糙集和互信息的特征选择方法。每个特征的依赖性指导选择，并利用互信息来减少不利于增加依赖性显着的特征。因此，通过我们的方法找到的子集的依赖性达到最大值与少量的功能。由于我们的方法通过依赖性和基于类的距离度量的组合选择标准来评估确定相关性和不确定相关性，因此特征子集比其他基于粗糙集的方法更相关。因此，该子集是接近最优解。为了验证的贡献，八个不同的分类应用程序。我们的方法也被用于一个真实的阿尔茨海默氏病数据集，并找到一个特征子集的分类准确率达到81.3%。这些结果验证了我们的方法的贡献。
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.
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