{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:18:17Z","timestamp":1780550297131,"version":"3.54.1"},"reference-count":22,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,5,27]],"date-time":"2019-05-27T00:00:00Z","timestamp":1558915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MIST\/IITP","award":["2018-0-00242"],"award-info":[{"award-number":["2018-0-00242"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/computers8020042","type":"journal-article","created":{"date-parts":[[2019,5,27]],"date-time":"2019-05-27T11:19:27Z","timestamp":1558955967000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Improved Measures of Redundancy and Relevance for mRMR Feature Selection"],"prefix":"10.3390","volume":"8","author":[{"given":"Insik","family":"Jo","sequence":"first","affiliation":[{"name":"Department of Data Science, Dankook University, Yongin 16890, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangbum","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Software Science, Dankook University, Yongin 16890, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sejong","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Software Science, Dankook University, Yongin 16890, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ins.2015.02.031","article-title":"A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure","volume":"307","author":"Wang","year":"2015","journal-title":"Inf. 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Available online: http:\/\/archive.ics.uci.edu\/ml\/."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/8\/2\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:55:27Z","timestamp":1760187327000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/8\/2\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,27]]},"references-count":22,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["computers8020042"],"URL":"https:\/\/doi.org\/10.3390\/computers8020042","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,27]]}}}