Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes

Research article (Pattern Recognition, 2020) · cited 57× · AI/ML
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Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes

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Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes. Retrieved May 24, 2026, from https://4ort.xyz/entity/accelerating-information-entropy-based-feature-selection-using-rough-set-theory-with-classified-nested-equivalence-class
MLA “Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/accelerating-information-entropy-based-feature-selection-using-rough-set-theory-with-classified-nested-equivalence-class.
BibTeX @misc{4ortxyz_accelerating-information-entropy-based-feature-selection-using-rough-set-theory-with-classified-nested-equivalence-class_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes}}, year = {2026}, url = {https://4ort.xyz/entity/accelerating-information-entropy-based-feature-selection-using-rough-set-theory-with-classified-nested-equivalence-class}, note = {Accessed: 2026-05-24}}
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