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To address this issue, a novel algorithm based on feature rank aggregation and graph theoretic technique for ensemble feature selection (R-GEFS) with the fusion of Pearson and Spearman correlation metrics is proposed. The method works by aggregation of the profile of preferences of five feature rankers as the base feature selectors. Then similar features are grouped into clusters using graph theoretic approach. The most representative feature strongly co-related to target decision classes is drawn from each cluster. The efficiency and effectiveness of the R-GEFS algorithm are evaluated through an empirical study. Extensive experiments on 15 diverse benchmark datasets are carried out to compare R-GEFS with seven state-of-the-art feature selection models with respect to four popular classifiers, namely decision tree, k nearest neighbor, random forest, and support vector machine. The proposed method turns out to be effective by selecting smaller feature subsets with lesser computational complexities and it assists in increasing the classification accuracy. <\/jats:p>","DOI":"10.1142\/s021800142250032x","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T10:14:09Z","timestamp":1651140849000},"source":"Crossref","is-referenced-by-count":7,"title":["R-GEFS: Condorcet Rank Aggregation with Graph Theoretic Ensemble Feature Selection Algorithm for Classification"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6294-0231","authenticated-orcid":false,"given":"Rubul Kumar","family":"Bania","sequence":"first","affiliation":[{"name":"Department of Computer Application, North-Eastern Hill University, Tura Campus, Meghalaya 794002, India"}]}],"member":"219","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"issue":"19","key":"S021800142250032XBIB001","first-page":"1","volume":"6","author":"Ahmed A.","year":"2014","journal-title":"J. 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