Version 1
: Received: 30 July 2020 / Approved: 5 August 2020 / Online: 5 August 2020 (10:28:20 CEST)
How to cite:
Yu, P.; Wang, H.; Li, J.; Lin, G. Matrix-based Approaches for Updating Approximations in Neighborhood Multigranulation Rough Sets while Neighborhood Classes Decreasing or Increasing. Preprints2020, 2020080125. https://doi.org/10.20944/preprints202008.0125.v1
Yu, P.; Wang, H.; Li, J.; Lin, G. Matrix-based Approaches for Updating Approximations in Neighborhood Multigranulation Rough Sets while Neighborhood Classes Decreasing or Increasing. Preprints 2020, 2020080125. https://doi.org/10.20944/preprints202008.0125.v1
Yu, P.; Wang, H.; Li, J.; Lin, G. Matrix-based Approaches for Updating Approximations in Neighborhood Multigranulation Rough Sets while Neighborhood Classes Decreasing or Increasing. Preprints2020, 2020080125. https://doi.org/10.20944/preprints202008.0125.v1
APA Style
Yu, P., Wang, H., Li, J., & Lin, G. (2020). Matrix-based Approaches for Updating Approximations in Neighborhood Multigranulation Rough Sets while Neighborhood Classes Decreasing or Increasing. Preprints. https://doi.org/10.20944/preprints202008.0125.v1
Chicago/Turabian Style
Yu, P., Jinjin Li and Guoping Lin. 2020 "Matrix-based Approaches for Updating Approximations in Neighborhood Multigranulation Rough Sets while Neighborhood Classes Decreasing or Increasing" Preprints. https://doi.org/10.20944/preprints202008.0125.v1
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.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
