Affiliations: [a] School of Mathematics and Statistics, Minnan normal university, Fujian, Zhangzhou, China
| [b]
Georgetown University, Georgetown, Washington DC, USA
Correspondence:
[*]
Corresponding author. Hongkun Wang, Georgetown University, Georgetown, Washington DC, 20057, USA. E-mail: Hongkun.Wang@georgetown.edu.
Abstract: With the revolution of computing and biology technology, data sets containing information could be huge and complex that sometimes are difficult to handle. Dynamic computing is an efficient approach to solve some of the problems. Since neighborhood multigranulation rough sets(NMGRS) were proposed, few papers focused on how to calculate approximations in NMGRS and how to update them dynamically. Here we propose approaches for computing approximations in NMGRS and updating them dynamically. First, static approaches for computing approximations in NMGRS are proposed. Second, search region in data set for updating approximations in NMGRS is shrunk. Third, matrix-based approaches for updating approximations in NMGRS while decreasing or increasing neighborhood classes are proposed. Fourth, incremental algorithms for updating approximations in NMGRS while decreasing or increasing neighborhood classes are designed. Finally, the efficiency and validity of the designed algorithms are verified by experiments.
Keywords: Approximation computation, multigranulation rough set, knowledge
acquisition, decision making
