{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:47:58Z","timestamp":1760147278705,"version":"build-2065373602"},"reference-count":108,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T00:00:00Z","timestamp":1674345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Key Project in Ningxia, China","award":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"],"award-info":[{"award-number":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"],"award-info":[{"award-number":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Construction Project of First-class Subjects in Ningxia Higher Education, China","award":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"],"award-info":[{"award-number":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"]}]},{"name":"Major Proprietary Funded Project of North Minzu University, China","award":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"],"award-info":[{"award-number":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"]}]},{"name":"Basic discipline research projects supported by Nanjing Securities","award":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"],"award-info":[{"award-number":["2022AAC02043","11961001","61561001","NXYLXK2017B09","ZDZX201901","NJZQJCXK202201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The \u201cCurse of Dimensionality\u201d induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. Feature selection (FS) can eliminate irrelevant information in big data and improve accuracy. As a recently proposed algorithm, the Sparrow Search Algorithm (SSA) shows its advantages in the FS tasks because of its superior performance. However, SSA is more subject to the population\u2019s poor diversity and falls into a local optimum. Regarding this issue, we propose a variant of the SSA called the Tent L\u00e9vy Flying Sparrow Search Algorithm (TFSSA) to select the best subset of features in the wrapper-based method for classification purposes. After the performance results are evaluated on the CEC2020 test suite, TFSSA is used to select the best feature combination to maximize classification accuracy and simultaneously minimize the number of selected features. To evaluate the proposed TFSSA, we have conducted experiments on twenty-one datasets from the UCI repository to compare with nine algorithms in the literature. Nine metrics are used to evaluate and compare these algorithms\u2019 performance properly. Furthermore, the method is also used on the coronavirus disease (COVID-19) dataset, and its classification accuracy and the average number of feature selections are 93.47% and 2.1, respectively, reaching the best. The experimental results and comparison in all datasets demonstrate the effectiveness of our new algorithm, TFSSA, compared with other wrapper-based algorithms.<\/jats:p>","DOI":"10.3390\/sym15020316","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T03:26:41Z","timestamp":1674444401000},"page":"316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Tent L\u00e9vy Flying Sparrow Search Algorithm for Wrapper-Based Feature Selection: A COVID-19 Case Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6762-1528","authenticated-orcid":false,"given":"Qinwen","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2021-2097","authenticated-orcid":false,"given":"Yuelin","family":"Gao","sequence":"additional","affiliation":[{"name":"Ningxia Key Laboratory of Intelligent Information and Big Data Processing, Yinchuan 750021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4313-8312","authenticated-orcid":false,"given":"Yanjie","family":"Song","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106553","DOI":"10.1016\/j.knosys.2020.106553","article-title":"A hyper learning binary dragonfly algorithm for feature selection: A COVID-19 case study","volume":"212","author":"Too","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","first-page":"57","article-title":"Knowledge discovery in databases: An overview","volume":"13","author":"Frawley","year":"1992","journal-title":"AI Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cios, K.J., Pedrycz, W., and Swiniarski, R.W. 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