图灵公式视角下的一种熵特征选择方法
An entropic feature selection method in perspective of Turing formula
AI总结：
针对健康数据类型复杂且样本量小的问题，提出基于覆盖度调整标准化互信息（CASMI）的特征选择方法，利用改进的熵估计器高效选特征并自动学习数量，在小样本下表现优异。
摘要：
健康数据通常类型复杂且样本量小。这种特定领域的挑战使得难以可靠地捕获信息，并进一步加剧了泛化问题。为了辅助医疗数据集的分析，我们基于覆盖度调整标准化互信息（CASMI）的概念开发了一种特征选择方法。该方法的主要优点是：1）借助改进的熵估计器更高效地选择特征，特别是在样本量较小时；2）基于样本数据的信息自动学习要选择的特征数量。此外，该方法从联合分布的角度处理特征冗余。该方法专注于非有序数据，同时通过适当的分箱方法处理数值数据。将该方法与六种广泛引用的特征选择方法进行比较的模拟研究表明，在以信息恢复率衡量时，所提方法表现更好，尤其是在样本量较小时。
英文摘要：
Health data are generally complex in type and small in sample size. Such domain-specific challenges make it difficult to capture information reliably and contribute further to the issue of generalization. To assist the analytics of healthcare datasets, we develop a feature selection method based on the concept of Coverage Adjusted Standardized Mutual Information (CASMI). The main advantages of the proposed method are: 1) it selects features more efficiently with the help of an improved entropy estimator, particularly when the sample size is small, and 2) it automatically learns the number of features to be selected based on the information from sample data. Additionally, the proposed method handles feature redundancy from the perspective of joint-distribution. The proposed method focuses on non-ordinal data, while it works with numerical data with an appropriate binning method. A simulation study comparing the proposed method to six widely cited feature selection methods shows that the proposed method performs better when measured by the Information Recovery Ratio, particularly when the sample size is small.
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