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CASMI—An Entropic Feature Selection Method in Turing’s Perspective

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Department of Mathematics and Statistics, Mississippi State University, Mississippi State, Starkville, MS 39762, USA
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Department of Software and Information Systems, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1179; https://doi.org/10.3390/e21121179
Received: 6 November 2019 / Revised: 23 November 2019 / Accepted: 23 November 2019 / Published: 29 November 2019
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. View Full-Text
Keywords: feature selection; small sample; healthcare dataset; sample coverage; entropy estimation feature selection; small sample; healthcare dataset; sample coverage; entropy estimation
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Shi, J.; Zhang, J.; Ge, Y. CASMI—An Entropic Feature Selection Method in Turing’s Perspective. Entropy 2019, 21, 1179.

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