Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Matrix-based Approaches for Updating Approximations in Neighborhood Multigranulation Rough Sets while Neighborhood Classes Decreasing or Increasing

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. Preprints 2020, 2020080125 (doi: 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 (doi: 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.

Subject Areas

Approximation computation; Multigranulation rough set; Knowledge acquisition; Decision making;

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