Author and reference Application field Type of study Study category Study contribution Jensen et al. [83 ] Feature selection Proposed Fuzzy-rough sets Proposed a model by using rough sets for solving problems related to the propositional satisfiability perspective Qian et al. [84 ] Feature selection Proposed Fuzzy-rough feature selection Proposed an approach based on dimensionality reduction together with sample reduction for a heuristic process of fuzzy-rough feature selection Hu et al. [85 ] Feature evaluation and selection Developed Fuzzy-rough sets Proposed soft fuzzy-rough sets by developing rough sets to reduce the influence of noise Hong et al. [86 ] Machine learning Proposed Fuzzy-rough sets Proposed the learning algorithm from the incomplete quantitative data sets based on rough sets Onan [87 ] Machine learning Proposed Fuzzy-rough sets Introduced a new classification approach based on the fuzzy-rough nearest neighbor method for the selection of fuzzy-rough instances Derrac et al. [88 ] Feature selection Proposed Fuzzy-rough sets Presented a new hybrid algorithm for reduction of data by using feature and instance selection Jensen and Mac Parthaláin [89 ] Feature selection Proposed Fuzzy-rough sets Introduced two novel diverse ways by using an attribute and neighborhood approximation step for solving problems of complexity of the subset evaluation metric Pal et al. [90 ] Feature selection Proposed Fuzzy-rough sets Proposed a new rough fuzzy approach for pattern classification based on granular computing Zhang et al. [24 ] Feature selection Proposed Fuzzy-rough sets Proposed a new fuzzy-rough set based on information entropy for feature selection Maji and Garai [91 ] Feature selection Developed Fuzzy-rough sets Presented a new feature selection approach based on fuzzy-rough sets by maximizing significant and relevance of the selected features Ganivada et al. [92 ] Feature selection Proposed Fuzzy-rough sets Proposed the granular neural network for recognizing salient features of data, based on fuzzy sets and a fuzzy-rough set Wang et al. [20 ] Feature subset selection Proposed Fuzzy-rough sets Proposed a new rough set approach for feature subset selection Kumar et al. [93 ] Feature selection Proposed Fuzzy-rough sets Proposed a novel algorithm based on fuzzy-rough sets for future selection and classification of datasets with multifeatures Hu et al. [94 ] Feature selection Proposed Fuzzy-rough sets Proposed two kinds of kernelized fuzzy-rough sets by integrating kernel functions and fuzzy-rough set approaches Cornelis et al. [95 ] Attribute selection Developed Fuzzy-rough sets Introduced and extended a new rough set theory based on multiadjoint fuzzy-rough sets for calculating the lower and upper approximations Mac Parthaláin and Jensen [96 ] Attribute reduction Proposed Fuzzy-rough sets Presented various several unsupervised feature selection (FS) approaches based on fuzzy-rough sets Dai and Xu [97 ] Attribute selection Proposed Fuzzy-rough sets Proposed the attribute selection method based on fuzzy-rough sets for tumor classification Chen et al. [98 ] Support vector machines (SVMs) Developed Fuzzy-rough sets Improved the hard margin support vector machines based on fuzzy-rough sets and a training membership sample in the constraints Hu et al. [99 ] Support vector machine Proposed Fuzzy-rough sets Proposed a novel approach for a fuzzy-rough model which was named soft fuzzy-rough sets for robust classification based on the approach Wei et al. [100 ] Attribute reduction Proposed Fuzzy-rough sets Introduced two kinds of fuzzy-rough approximations and defined two corresponding relative positive region reducts Yao et al. [101 ] Attribute reduction Proposed Fuzzy-rough sets Introduced a new expanded fuzzy-rough approach which was named the variable precision Chen et al. [102 ] Attribute reduction Developed Fuzzy-rough sets Developed a new algorithm for finding reduction based on the minimal factors in the discernibility matrix Zhao et al. [103 ] Attribute reduction Proposed Fuzzy-rough sets Introduced the robust model of dimension reduction by using fuzzy-rough sets for (reflecting of the reducts) achieved on the possible parameters Chen and Yang [104 ] Attribute reduction Developed Fuzzy-rough sets Integrating the rough set and fuzzy-rough set model for attribute reduction in decision systems with real and symbolic valued condition attributes
