Bi-objective feature selection in high-dimensional datasets using improved binary chimp optimization algorithm
The machine learning process in high-dimensional datasets is far more complicated than in low-dimensional datasets. In high-dimensional datasets, Feature Selection (FS) is necessary to decrease the complexity of learning. However, FS in high-dimensional datasets is a complex process that requires th...
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| Published in: | International journal of machine learning and cybernetics Vol. 15; no. 12; pp. 6107 - 6148 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2024
Springer Nature B.V |
| Subjects: | |
| ISSN: | 1868-8071, 1868-808X |
| Online Access: | Get full text |
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