Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
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DOI：
10.1109/tnn.2004.841414
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发表时间：
2005-01-01
影响因子：
--
通讯作者：
Huang, D
中科院分区：
文献类型：
--
作者：
Chow, TWS;Huang, D
提出了一种基于互信息（MI）的特征选择方法。在所有基于MI的特征选择方法中，高维MI的有效估计至关重要。本文将一种剪枝Parzen窗估计与二次互信息（QMI）相结合来解决这一问题。实验结果表明，该方法能够有效地估计出故障点。在此基础上，提出了一种新的特征选择方法来逐个识别显著特征。此外，还可以可靠地估计用于分类的适当特征子集。所提出的方法在四种不同的分类应用中进行了彻底的测试，其中特征的数量从少于10个到超过15000个不等。所提出的结果是非常有希望的，并证实了所提出的特征选择方法的贡献。
A novel feature selection method using the concept of mutual information (MI) is proposed in this paper. In all MI based feature selection methods, effective and efficient estimation of high-dimensional MI is crucial. In this paper, a pruned Parzen window estimator and the quadratic mutual information (QMI) are combined to address this problem. The results show that the proposed approach can estimate the MI in an effective and efficient way. With this contribution, a novel feature selection method is developed to identify the salient features one by one. Also, the appropriate feature subsets for classification can be reliably estimated. The proposed methodology is thoroughly tested in four different classification applications in which the number of features ranged from less than 10 to over 15000. The presented results are very promising and corroborate the contribution of the proposed feature selection methodology.