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.
翻译:健康数据在类型上一般比较复杂,抽样规模较小。这种特定领域的挑战使得难以可靠地收集信息,难以为一般化问题进一步作出贡献。为了协助分析保健数据集,我们根据覆盖调整标准化的相互信息概念,制定了一种特征选择方法。拟议方法的主要优点是:(1) 它在改进了的酶测量器的帮助下,特别是当样本规模较小时,选择了更高效的特征;(2) 它自动地了解根据抽样数据信息选择的特征数量。此外,拟议方法从联合分配的角度处理特征冗余。拟议方法侧重于非标准数据,同时用适当的宾式方法处理数字数据。将拟议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,在用信息恢复率衡量时,拟议方法效果更好,特别是当样本规模小时。