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Improved Measures of Redundancy and Relevance for mRMR Feature Selection

1
Department of Data Science, Dankook University, Yongin 16890, Korea
2
Department of Software Science, Dankook University, Yongin 16890, Korea
*
Author to whom correspondence should be addressed.
Computers 2019, 8(2), 42; https://doi.org/10.3390/computers8020042
Received: 4 April 2019 / Revised: 29 April 2019 / Accepted: 22 May 2019 / Published: 27 May 2019
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Abstract

Many biological or medical data have numerous features. Feature selection is one of the data preprocessing steps that can remove the noise from data as well as save the computing time when the dataset has several hundred thousand or more features. Another goal of feature selection is improving the classification accuracy in machine learning tasks. Minimum Redundancy Maximum Relevance (mRMR) is a well-known feature selection algorithm that selects features by calculating redundancy between features and relevance between features and class vector. mRMR adopts mutual information theory to measure redundancy and relevance. In this research, we propose a method to improve the performance of mRMR feature selection. We apply Pearson’s correlation coefficient as a measure of redundancy and R-value as a measure of relevance. To compare original mRMR and the proposed method, features were selected using both of two methods from various datasets, and then we performed a classification test. The classification accuracy was used as a measure of performance comparison. In many cases, the proposed method showed higher accuracy than original mRMR. View Full-Text
Keywords: feature selection; feature evaluation; classification accuracy; redundancy; relevance; mRMR feature selection; feature evaluation; classification accuracy; redundancy; relevance; mRMR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Jo, I.; Lee, S.; Oh, S. Improved Measures of Redundancy and Relevance for mRMR Feature Selection. Computers 2019, 8, 42.

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