Next Article in Journal
Autonomous Wireless Sensor Networks in an IPM Spatial Decision Support System
Previous Article in Journal
A Sparse Analysis-Based Single Image Super-Resolution
Open AccessArticle

Improved Measures of Redundancy and Relevance for mRMR Feature Selection

by Insik Jo 1, Sangbum Lee 2 and Sejong Oh 2,*
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
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
Show Figures

Graphical abstract

MDPI and ACS Style

Jo, I.; Lee, S.; Oh, S. Improved Measures of Redundancy and Relevance for mRMR Feature Selection. Computers 2019, 8, 42.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop