… subsets due to the perturbations from redundant features. This paper delves into Student-t
kernel and fuzzy divergence to address these challenges for fuzzy rough feature selection. …

… The rest of the work is organized as follows: In Section II, materials and methods employed
in this paper like dataset, feature extraction, feature selection, classification protocol, and …

… In this section, an application of divergence based fuzzy rough sets to feature selection is …
study of the feature selection method using the divergence based fuzzy positive region. Eleven …

… Within the framework of divergence-based fuzzy rough sets (Div-FRSs), we implement …
establish double-quantitative feature selection, and two novel approaches of feature selection (ie, …

… to be effective for selecting nonredundant and relevant features of real-valued … feature
selection in fuzzy approximation spaces. Some quantitative indexes are introduced based on fuzzy…

… Fuzzy-rough set-based feature (FS) selection has been shown to be highly useful at …
approaches to fuzzy-rough FS-based on fuzzy similarity relations. In particular, a fuzzy extension to …

… We present a feature selection for FCM. The advantage of our method is that it is intuitively
… ρl, we adopt the idea presented in [7] to define the feature saliency KL divergence. This …

… Feature selection facilitates uncertainty disposal and information … Divergence-based fuzzy
rough sets (Div-FRSs), a new kind of fuzzy rough sets, have been applied to feature selection …

… the feature selection in fuzzy … fuzzy data. In this process, to fully consider the distribution
characteristics of unbalanced data, we construct a local composite entropy for handling the fuzzy …

… a new divergence-based fuzzy neighborhood rough set … divergence-based fuzzy neighborhood
relation and class are gradually raised to manage the noise in multi-label data, and fuzzy …