Double Filter and Double Wrapper Feature Selection Algorithm for High-Dimensional Data Analysis
With the advent of the big data era, we often deal with datasets containing a large number of redundant features, and in this context, dimensionality reduction of data becomes crucial. To address this issue, this study proposes a double filter and double wrapper (DFDW) feature selection algorithm fo...
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| Autors principals: | , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
IEEE
2025-01-01
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| Col·lecció: | IEEE Access |
| Matèries: | |
| Accés en línia: | https://ieeexplore.ieee.org/document/11002483/ |
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