The existing fuzzy rough set (FRS) models all believe that the decision attribute divides sample into several “clear” classes, and this data processing method makes model sensitive to noise information when conducting feature selection. To solve problem, paper proposes a robust (RS-FRS) based on representative samples. Firstly, membership degree of samples is defined reflect its fuzziness uncer...

Aiming at solving the uncertainty of knowledge in variable precision fuzzy soft rough set model. Firstly, from perspectives approximated and equivalent approximation space, measurement methods model are defined, such as information granularity, entropy, etc; Secondly, many related theorems given proved, monotonicity obtained when relation satisfies inclusion condition; Finally, feasibility vali...

Multigranulation rough set theory is one of the most effective tools for data analysis and mining in multicriteria information systems. Six types covering-based multigranulation fuzzy (CMFRS) models have been constructed through β -neighborhoods or measures. However, it often time-consuming to compute these CMFRS with a ...

A new coding/decoding scheme based on the properties and operations of rough fuzzy sets is presented. By normalizing pixel values of an image, each pixel value can be interpreted as the degree of belonging of that pixel to the image foreground. The image is then subdivided into blocks which are partitioned and characterized by a pair of approximation sets. Coding uses a codebook, created with a...

Fuzzy rough set theory is a candidate framework for query refinement. Indeed, a thesaurus defines an approximation space in which the query, which is a set of terms, can be approximated from the upper and the lower side. The upper approximation turns out to be too flexible however, resulting in query explosion, while the lower approximation is too strict, resulting in the empty query. Therefore...