Rough set theory for the interval-valued fuzzy information systems

Fuzzy rough sets hybrid scheme for motion and scene complexity adaptive deinterlacing

“…For example, from Table 5, it is apparent that rule 7-14 and rule [15][16][17][18][19][20][21][22] are identical. Hence, it is sufficient to have one representative rule from each group of identical rules.…”

“…A technique that is not currently employed in video deinterlacing is the fuzzy-rough sets hybrid scheme, which has been researched by many authors [4,[11][12][13]17,18,[22][23][24]26,27,[32][33][34][35][36][37][38]44,50,52,59,60,65]. A number of algorithms for image processing exist.…”

“…Classification applications for the execution of the fuzzy rough sets system have began to appear [22,48,50,54]. Some contributions have been made in computer science applications [11][12][13]17,18,32,35,36,60,65]. However, attribute reduction has used only a rough sets system to date [23,24,26,27].…”

On characterization of intuitionistic fuzzy rough sets based on intuitionistic fuzzy implicators

“…More recently, rough set approximations have also been developed into the fuzzy environment, and the results are called rough fuzzy sets (fuzzy sets approximated by a crisp approximation space) [15,23,43,47,50] and fuzzy rough sets (fuzzy or crisp sets approximated by a fuzzy approximation space) [15,[26][27][28]30,36,46,48,50,58]. These models have been employed to handle fuzzy and quantitative data [17][18][19]21,40,45]. It can be observed that the constructive approach is suitable for practical applications of rough sets, and hence the majority of studies on rough sets have concentrated on the constructive approach.…”

Attribute reduction and optimal decision rules acquisition for continuous valued information systems

“…By combining the rough set theory with the interval valued fuzzy set theory, Gong et al in [5] developed an interval valued rough fuzzy set model to deal with interval valued fuzzy information systems with crisp condition attributes and fuzzy interval valued decision attributes. For the knowledge discovery problem, they [5] presented an approach to deduce fuzzy decision rules from the initial data, but they did not investigate the knowledge reduction and optimal decision rules acquisition. Sun et al in [34] established an interval valued fuzzy rough set model for interval valued fuzzy information systems, and investigated the knowledge reduction problem.…”