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