Matrix-Based Approaches for Updating Approximations in Multigranulation Rough Set While Adding and Deleting Attributes

DOI

10.2991/ijcis.d.190718.001How to use a DOI?

Keywords

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

Abstract

With advanced technology in medicine and biology, data sets containing information could be huge and complex that sometimes are difficult to handle. Dynamic computing is an efficient approach to solve some problems. Since multigranulation rough sets were proposed, many algorithms have been designed for updating approximations in multigranulation rough sets, but they are not efficient enough in terms of computational time. The purpose of this study is to further reduce the computational time of updating approximations in multigranulation rough sets. First, searching regions in data sets for updating approximations in multigranulation rough sets are shrunk. Second, matrix-based approaches for updating approximations in multigranulation rough set are proposed. The incremental algorithms for updating approximations in multigranulation rough sets are then designed. Finally, the efficiency and validity of the designed algorithms are verified by experiments.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Peiqiu Yu
AU  - Jinjin Li
AU  - Hongkun Wang
AU  - Guoping Lin
PY  - 2019
DA  - 2019/07/05
TI  - Matrix-Based Approaches for Updating Approximations in Multigranulation Rough Set While Adding and Deleting Attributes
JO  - International Journal of Computational Intelligence Systems
SP  - 855
EP  - 872
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190718.001
DO  - 10.2991/ijcis.d.190718.001
ID  - Yu2019
ER  -