{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T06:45:00Z","timestamp":1762065900837,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T00:00:00Z","timestamp":1661817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"],"award-info":[{"award-number":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"]}]},{"name":"Key Research Project of Shaanxi Province","award":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"],"award-info":[{"award-number":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"]}]},{"name":"Natural Science Foundation of Shaanxi Province of China","award":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"],"award-info":[{"award-number":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"]}]},{"name":"Shaanxi Youth Talent Lifting Plan of Shaanxi Association for Science and Technology","award":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"],"award-info":[{"award-number":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"]}]},{"name":"Special Scientific Research Plan Project of the Shaanxi Province Education Department","award":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"],"award-info":[{"award-number":["51875457","2022GY-050","2022GY-028","2022JQ-636","2021JQ-701","20220129","21JK0905"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Extreme learning machines (ELMs) have gained acceptance owing to their high efficiency and outstanding generalization ability. As a key component of data preprocessing, feature selection methods can decrease the noise or irrelevant data for ELMs. However, ELMs still do not have their own practical feature selection method for their special mechanism. In this study, we proposed a feature selection method for the ELM, named FELM. The proposed algorithm achieves highly efficient dimensionality reduction due to the feature ranking strategy. The FELM can simultaneously complete the feature selection and classification processes. In addition, by incorporating a memorization\u2013generalization kernel into the FELM, the nonlinear case of it is issued (called FKELM). The FKELM can achieve high classification accuracy and extensive generalization by applying the property of memorization of training data. According to the experimental results on different artificial and benchmark datasets, the proposed algorithms achieve significantly better classification accuracy and faster training than the other methods.<\/jats:p>","DOI":"10.3390\/axioms11090444","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T21:25:18Z","timestamp":1661894718000},"page":"444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Feature Selection Methods for Extreme Learning Machines"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1937-2990","authenticated-orcid":false,"given":"Yanlin","family":"Fu","sequence":"first","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Control and Intelligent Process, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haotian","family":"Gao","sequence":"additional","affiliation":[{"name":"No. 92677 Troops of PLA, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3233\/IDA-1997-1302","article-title":"Feature selection for classification","volume":"1","author":"Dash","year":"1997","journal-title":"Intell. 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