Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization
Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization
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Basel: MDPI AG
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English
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Basel: MDPI AG
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Feature selection (FS) methods play essential roles in different machine learning applications. Several FS methods have been developed; however, those FS methods that depend on metaheuristic (MH) algorithms showed impressive performance in various domains. Thus, in this paper, based on the recent advances in MH algorithms, we introduce a new FS tec...
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Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization
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TN_cdi_doaj_primary_oai_doaj_org_article_27c5eb3d8746407988ea7a7c80025149
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_27c5eb3d8746407988ea7a7c80025149
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ISSN
2227-7390
E-ISSN
2227-7390
DOI
10.3390/math10234565
