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This paper demonstrates that by optimizing the fitness function, swarm algorithms are capable to select the best subset of features. Further, in this paper, an attempt has been made to improve the capability of the swarm based algorithms such as Intelligent Dynamic Swarm (IDS) and Particle Swarm Optimization (PSO) through modified initialization of solutions, for picking the appropriate features for the feature selection task. Improvement in the size of reducts and classification accuracy of these reducts are observed when initialization is done using the proposed method. Statistical t-tests have also been performed for the validation of the results.<\/jats:p>","DOI":"10.3233\/jifs-182606","type":"journal-article","created":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T11:50:27Z","timestamp":1561117827000},"page":"1155-1164","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Intelligent fuzzy rough set based feature selection using swarm algorithms with improved initialization"],"prefix":"10.1177","volume":"37","author":[{"given":"Tarun","family":"Maini","sequence":"first","affiliation":[{"name":"Systems Engineering, Department of Electrical Engineering, IIT(BHU) Varanasi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhishek","family":"Kumar","sequence":"additional","affiliation":[{"name":"Systems Engineering, Department of Electrical Engineering, IIT(BHU) Varanasi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rakesh Kumar","family":"Misra","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, IIT(BHU) Varanasi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Devender","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, IIT(BHU) Varanasi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,6,20]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(01)00032-X"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.03.016"},{"key":"e_1_3_1_4_2","article-title":"A fitting model for feature selection with fuzzy rough sets","author":"Wang C.","year":"2016","unstructured":"WangC., QiY., ShaoM., HuQ., ChenD., QianY. and LinY., A fitting model for feature selection with fuzzy rough sets, IEEE Transactions on Fuzzy Systems 2016.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/03081079008935107"},{"key":"e_1_3_1_6_2","first-page":"44","article-title":"Research on feature extraction from remote sensing image","volume":"1","author":"Ren D.","year":"2010","unstructured":"RenD. and MaA.Y., Research on feature extraction from remote sensing image, International Conference in Computer Application and System Modeling 1 (2010), 44\u201348.","journal-title":"International Conference in Computer Application and System Modeling"},{"key":"e_1_3_1_7_2","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon I.","year":"2003","unstructured":"GuyonI. and ElisseeffA., An introduction to variable and feature selection, The Journal of Machine Learning Research 3 (2003), 1157\u20131182.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_1_8_2","article-title":"Data mining: Practical machine learning tools with java implementations","author":"Witten I.","year":"2000","unstructured":"WittenI. and FrankE., Data mining: Practical machine learning tools with java implementations, M. 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