Matrix-based optimistic multigranulation fuzzy covering rough sets

Jun-chao, Wei; Chang, Zaibin; Mao, Lingling

doi:10.1109/icbaie52039.2021.9390045

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proposition 3 illustrates that the matrix representation of all multigranulation fuzzy measures degrees is symmetric. Proposition 4 (see [37]).…”

Section: Journal Of Mathematicsmentioning

confidence: 99%

See 1 more Smart Citation

Matrix Approaches for Covering-Based Multigranulation Fuzzy Rough Set Models

Chang¹,

Jun-chao²

2023

Journal of Mathematics

View full text Add to dashboard Cite

Multigranulation rough set theory is one of the most effective tools for data analysis and mining in multicriteria information systems. Six types of covering-based multigranulation fuzzy rough set (CMFRS) models have been constructed through fuzzy β -neighborhoods or multigranulation fuzzy measures. However, it is often time-consuming to compute these CMFRS models with a large fuzzy covering using set representation approaches. Hence, presenting novel methods to compute them quickly is our motivation for this paper. In this article, we study the matrix representations of CMFRS models to save time in data processing. Firstly, some new matrices and matrix operations are proposed. Then, matrix representations of optimistic CMFRSs are presented. Moreover, matrix approaches for computing pessimistic CMFRSs are also proposed. Finally, some experiments are proposed to illustrate the effectiveness of our approaches.

show abstract

“…Proposition 3 illustrates that the matrix representation of all multigranulation fuzzy measures degrees is symmetric. Proposition 4 (see [37]).…”

Section: Journal Of Mathematicsmentioning

confidence: 99%

“…Inspired by [37], we present matrix representations for computing CMFRS models [32] in this paper. Moreover, the efectiveness of our matrix approaches is given by some experiments.…”

Section: Introductionmentioning

confidence: 99%

Matrix Approaches for Covering-Based Multigranulation Fuzzy Rough Set Models

Chang¹,

Jun-chao²

2023

Journal of Mathematics

View full text Add to dashboard Cite

show abstract

Brain Tumor Early Diagnosis Using Hybrid Fuzzy K-Means and Convolutional Neural Networks

Jeyavani

Karuppasamy

2023

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

An interval rough number variable precision rough sets model and its attribute reduction

Liu

et al. 2023

IFS

View full text Add to dashboard Cite

The interval rough number rough sets model is the generalization of the classical rough sets. Since the lower approximation condition of interval rough number rough sets model is a full inclusion relation which is too strict to tolerate noisy data, strict conditions increase the possibility of a sample classified into a wrong class. To overcome the above shortcomings, an interval rough number variable precision rough sets model is proposed in this paper, which is combined with interval rough number similarity and the concept of variable precision rough sets. The model introduces the error parameter and can improve the tolerance of noise data. Then the related properties of the model are also proved. Moreover, we construct a maximal positive domain attribute reduction method based on the proposed model, which can process the data type of interval rough number without discretization. Finally, numerical examples are given to verify the rationality of the model.

show abstract

Matrix-based optimistic multigranulation fuzzy covering rough sets

Cited by 3 publications

References 14 publications

Matrix Approaches for Covering-Based Multigranulation Fuzzy Rough Set Models

Matrix Approaches for Covering-Based Multigranulation Fuzzy Rough Set Models

Brain Tumor Early Diagnosis Using Hybrid Fuzzy K-Means and Convolutional Neural Networks

An interval rough number variable precision rough sets model and its attribute reduction

Contact Info

Product

Resources

About