Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains ...
Researchers at Yazd University developed a binary version of the Puma Optimization Algorithm that achieved 91.53 percent ...
A new multi-granularity fuzzy rough feature selection method fuses algebraic and informational views, improving classification accuracy across 20 benchmark datasets.
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