Fuzzy rough sets are important mathematical tool for processing data using existing knowledge. Fuzzy rough sets have been widely studied and used into various fields, such as data reduction and image processing, etc. In extensive literature we have studied, general quasi overlap functions and fuzzy neighborhood systems are broader than other all fuzzy operators and knowledge used in existing fuzzy rough sets, respectively. In this article, a novel fuzzy rough sets model (shortly (I,Q,NS)-fuzzy rough sets) is proposed using fuzzy implications, general quasi overlap functions and fuzzy neighborhood systems, which contains almost all existing fuzzy rough sets. Then, a novel feature selection algorithm (called IQNS-FS algorithm) is proposed and implemented using (I,Q,NS)-fuzzy rough sets, dependency and specificity measure. The results of 12 datasets indicate that IQNS-FS algorithm performs better than others. Finally, we input the results of IQNS-FS algorithm into single hidden layer neural networks and other classification algorithms, the results illustrate that the IQNS-FS algorithm can be better connected with neural networks than other classification algorithms. The high classification accuracy of single hidden layer neural networks (a very simple structure) further shows that the attributes selected by the IQNS-FS algorithm are important which can express the features of the datasets.

The relationships between T-rough sets and covering based rough sets are investigated, and two kinds of generated methods of rough approximation operators from existing rough sets are established. Moreover, applying the aforementioned generated methods of approximation operators, S-rough sets and s ome new covering-based rough sets are introduced and their basic properties are discussed.