Incomplete Variable Multigranulation Rough Sets Decision

Zhang, Ming; Wang, Xu; Yang, Xibei; Tang, Zhenmin

doi:10.12785/amis/080327

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…Yang et al [20,21] further explored the characteristics of MGRSs in incomplete information system. Zhang et al [22] put forth a frame work of variable MGRSs. Liu et al [23] introduced four different types of covering MGRS schemes using a covering approximation space.…”

Section: Introductionmentioning

confidence: 99%

A novel multigranulation roughness of bipolar fuzzy set over dual universes and its applications

2023

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In the world, multigranulation rough sets and bipolar fuzzy sets are the practical tools for dealing with the uncertainty, vagueness, and imperfection that frequently arise in decision mak- ing problems. The goal of multigranulation rough sets is to characterize uncertain situations in var- ious granularity spaces, whereas bipolar fuzzy sets (BFSs) have the capacity to handle uncertainty as well as informational bipolarity in a variety of contexts. This article examines the optimistic multigranulation rough approximation of a BFS in the light of multi-soft binary relations across two different universes. We investigate some algebraic properties of our newly constructed optimistic multigranulation rough set scheme. Measures of accuracy and roughness are also covered in this paper. Finally, two decision algorithms are designed with respect to aftersets and foresets that are applied to a decision making problem in disease diagnoses, and the applicability of the method is illustrated by a numerical example.

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Section: Introductionmentioning

confidence: 99%

A novel multigranulation roughness of bipolar fuzzy set over dual universes and its applications

2023

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“…Based on the PMGRS model, Qian et al [21] studied the attribute reduction from a decision table. Based on the MGRS theory, many models have been proposed to apply MGRS into a broader area, such as variable multigranulation rough sets [45], neighborhood multigranulation rough sets [14,16], incomplete multigranulation rough sets [22], variable precision multigranulation decision-theoretic fuzzy rough sets [3] and local multigranulation decision-theoretic rough sets [25].…”

Section: Introductionmentioning

confidence: 99%

Relative relation matrix-based approaches for updating approximations in multigranulation rough sets

Xian

Chen

2020

Filomat

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Multigranulation rough set (MGRS) theory has attracted much attention. However, with the advent of big data era, the attribute values may often change dynamically, which leads to high computational complexity when handling large and complex data. How to effectively obtain useful knowledge from the dynamic information system becomes an important issue in MGRS. Motivated by this requirement, in this paper, we propose relative relation matrix approaches for computing approximations in MGRS and updating them dynamically. A simplified relative relation matrix is used to calculate approximations in MGRS, it is showed that the space and time complexities are no more than that of the original method. Furthermore, relative relation matrix-based approaches for updating approximations in MGRS while refining or coarsening attribute values are proposed. Several incremental algorithms for updating approximations in MGRS are designed. Finally, experiments are conducted to evaluate the efficiency and validity of the proposed methods.

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“…Some professors consider fuzzy logic and fuzzy logic inference in [46][47][48]. The literature [49] extended two MGRS models into an incomplete information system. Yang [50] mainly researched several characteristics of MGRS in interval-valued information systems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Granulation Rough Set for Incomplete Interval-Valued Decision Information Systems Based on Multi-Threshold Tolerance Relation

Lin

2018

Symmetry

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A relation is viewed as a granularity from a granular computing perspective. A classic rough set contains only one granularity. A multi-granulation rough set contains multiple granularities, which promotes the applications of classical rough set. Firstly, this paper uses the incomplete interval-valued decision information system (IIVDIS) as research object and constructs two rough set models in the light of single granularity rough set model for applying the rough set theory to real life more widely, which are optimistic multi-granulation rough set (OMGRS) model and pessimistic multi-granulation rough set (PMGRS) model in the IIVDIS. Secondly, we design two algorithms to compute the roughness and the degree of dependence that are two tools for measuring uncertainty of rough set. Finally, several experiments are performed on six UCI data sets to verify the validity of the proposed theorems.

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Incomplete Variable Multigranulation Rough Sets Decision

Cited by 10 publications

References 32 publications

A novel multigranulation roughness of bipolar fuzzy set over dual universes and its applications

A novel multigranulation roughness of bipolar fuzzy set over dual universes and its applications

Relative relation matrix-based approaches for updating approximations in multigranulation rough sets

Multi-Granulation Rough Set for Incomplete Interval-Valued Decision Information Systems Based on Multi-Threshold Tolerance Relation

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