GBRS: An Unified Model of Pawlak Rough Set and Neighborhood Rough Set
- URL: http://arxiv.org/abs/2201.03349v2
- Date: Tue, 11 Jan 2022 02:04:25 GMT
- Title: GBRS: An Unified Model of Pawlak Rough Set and Neighborhood Rough Set
- Authors: Shuyin Xia, Cheng Wang, GuoYing Wang, XinBo Gao, Elisabeth Giem,
JianHang Yu
- Abstract summary: Pawlak rough set and neighborhood rough set are the two most common rough set theoretical models.
This paper presents a granular-ball rough set based on the granlar-ball computing.
- Score: 67.17936132922955
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pawlak rough set and neighborhood rough set are the two most common rough set
theoretical models. Pawlawk can use equivalence classes to represent knowledge,
but it cannot process continuous data; neighborhood rough sets can process
continuous data, but it loses the ability of using equivalence classes to
represent knowledge. To this end, this paper presents a granular-ball rough set
based on the granlar-ball computing. The granular-ball rough set can
simultaneously represent Pawlak rough sets, and the neighborhood rough set, so
as to realize the unified representation of the two. This makes the
granular-ball rough set not only can deal with continuous data, but also can
use equivalence classes for knowledge representation. In addition, we propose
an implementation algorithms of granular-ball rough sets. The experimental
resuts on benchmark datasets demonstrate that, due to the combination of the
robustness and adaptability of the granular-ball computing, the learning
accuracy of the granular-ball rough set has been greatly improved compared with
the Pawlak rough set and the traditional neighborhood rough set. The
granular-ball rough set also outperforms nine popular or the state-of-the-art
feature selection methods.
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