scholarly journals Granular Space Reduction to aβMultigranulation Fuzzy Rough Set

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Junyi Zhou ◽  
Shaohui Ma ◽  
Jianzhen Li
Keyword(s):  
Rough Set ◽  
Continuous Data ◽  
Data Sets ◽  
Fuzzy Rough Set ◽  
Space Reduction ◽  
Positive Region ◽  
Special Cases ◽  
Multigranulation Rough Set ◽  
Rough Theory

Multigranulation rough set is an extension of classical rough set, and optimistic multigranulation and pessimistic multigranulation are two special cases of it.βmultigranulation rough set is a more generalized multigranulation rough set. In this paper, we first introduce fuzzy rough theory intoβmultigranulation rough set to construct aβmultigranulation fuzzy rough set, which can be used to deal with continuous data; then some properties are discussed. Reduction is an important issue of multigranulation rough set, and an algorithm of granular space reduction toβmultigranulation fuzzy rough set for preserving positive region is proposed. To test the algorithm, experiments are taken on five UCI data sets with different values ofβ. The results show the effectiveness of the proposed algorithm.

scholarly journals About a Fuzzy Distance between Two Fuzzy Partitions and Application in Attribute Reduction Problem

2016 ◽  
Vol 16 (4) ◽  
pp. 13-28 ◽  
Author(s):  
Cao Chinh Nghia ◽  
Demetrovics Janos ◽  
Nguyen Long Giang ◽  
Vu Duc Thi
Keyword(s):  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Data Sets ◽  
Theory Approach ◽  
Fuzzy Rough Set ◽  
Fuzzy Distance ◽  
Reduction Methods ◽  
Fuzzy Partitions ◽  
Decision Tables

Abstract According to traditional rough set theory approach, attribute reduction methods are performed on the decision tables with the discretized value domain, which are decision tables obtained by discretized data methods. In recent years, researches have proposed methods based on fuzzy rough set approach to solve the problem of attribute reduction in decision tables with numerical value domain. In this paper, we proposeafuzzy distance between two partitions and an attribute reduction method in numerical decision tables based on proposed fuzzy distance. Experiments on data sets show that the classification accuracy of proposed method is more efficient than the ones based fuzzy entropy.

scholarly journals Some New Covering-Based Multigranulation Fuzzy Rough Sets and Corresponding Application in Multicriteria Decision Making

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Zaibin Chang ◽  
Lingling Mao
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Rough Sets ◽  
Large Scale ◽  
Rough Set Theory ◽  
Multicriteria Decision Making ◽  
Matrix Representations ◽  
Fuzzy Rough Set ◽  
Multicriteria Decision ◽  
Multigranulation Rough Set

Multigranulation rough set theory is an important tool to deal with the problem of multicriteria information system. The notion of fuzzy β -neighborhood has been used to construct some covering-based multigranulation fuzzy rough set (CMFRS) models through multigranulation fuzzy measure. But the β -neighborhood has not been used in these models, which can be seen as the bridge of fuzzy covering-based rough sets and covering-based rough sets. In this paper, the new concept of multigranulation fuzzy neighborhood measure and some types of covering-based multigranulation fuzzy rough set (CMFRS) models based on it are proposed. They can be seen as the further combination of fuzzy sets: covering-based rough sets and multigranulation rough sets. Moreover, they are used to solve the problem of multicriteria decision making. Firstly, the definition of multigranulation fuzzy neighborhood measure is given based on the concept of β -neighborhood. Moreover, four types of CMFRS models are constructed, as well as their characteristics and relationships. Then, novel matrix representations of them are investigated, which can satisfy the need of knowledge discovery from large-scale covering information systems. The matrix representations can be more easily implemented than set representations by computers. Finally, we apply them to manage the problem of multicriteria group decision making (MCGDM) and compare them with other methods.

scholarly journals An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

2021 ◽  
Vol 5 (3) ◽  
pp. 92-108
Author(s):  
Rana Aamir Raza
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Set Theory ◽  
Rough Set ◽  
Large Scale ◽  
Rough Set Theory ◽  
Classification Model ◽  
Data Sets ◽  
Fuzzy Rough Set ◽  
Random Weight

In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network).

Knowledge Acquisition of Interval Set-Valued Based on Granular Computing

2014 ◽  
Vol 543-547 ◽  
pp. 2017-2023
Author(s):  
Qing Guan ◽  
Jian He Guan
Keyword(s):  
Machine Learning ◽  
Knowledge Discovery ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Fuzzy Rough Set ◽  
Decision Systems ◽  
Machine Learning Methods ◽  
Rough Theory

The technique of a new extension of fuzzy rough theory using partition of interval set-valued is proposed for granular computing during knowledge discovery in this paper. The natural intervals of attribute values in decision system to be transformed into multiple sub-interval of [0,1]are given by normalization. And some characteristics of interval set-valued of decision systems in fuzzy rough set theory are discussed. The correctness and effectiveness of the approach are shown in experiments. The approach presented in this paper can also be used as a data preprocessing step for other symbolic knowledge discovery or machine learning methods other than rough set theory.

Multigranulation roughness based on soft relations

2021 ◽  
pp. 1-16
Author(s):  
Muhammad Shabir ◽  
Jamalud Din ◽  
Irfan Ahmad Ganie
Keyword(s):  
Finite Number ◽  
Rough Set ◽  
Equivalence Relations ◽  
Binary Relations ◽  
Soft Sets ◽  
Upper Approximation ◽  
Special Cases ◽  
Multigranulation Rough Set ◽  
Empty Subset ◽  
Lower And Upper Approximations

The original rough set model, developed by Pawlak depends on a single equivalence relation. Qian et al, extended this model and defined multigranulation rough sets by using finite number of equivalence relations. This model provide new direction to the research. Recently, Shabir et al. proposed a rough set model which depends on a soft relation from an universe V to an universe W . In this paper we are present multigranulation roughness based on soft relations. Firstly we approximate a non-empty subset with respect to aftersets and foresets of finite number of soft binary relations. In this way we get two sets of soft sets called the lower approximation and upper approximation with respect to aftersets and with respect to foresets. Then we investigate some properties of lower and upper approximations of the new multigranulation rough set model. It can be found that the Pawlak rough set model, Qian et al. multigranulation rough set model, Shabir et al. rough set model are special cases of this new multigranulation rough set model. Finally, we added two examples to illustrate this multigranulation rough set model.

scholarly journals Fuzzy Distance Based Attribute Reduction in Decision Tables

2017 ◽  
pp. 104
Author(s):  
Cao Chinh Nghia ◽  
Vu Duc Thi ◽  
Nguyen Long Giang ◽  
Tan Hanh
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Data Sets ◽  
Fuzzy Rough Set ◽  
Fuzzy Distance ◽  
Reduction Methods ◽  
Decision Tables ◽  
Attribute Value

In recent years, fuzzy rough set based attribute reduction has attracted the interest of many researchers. The attribute reduction methods can perform directly on the decision tables with numerical attribute value domain. In this paper, we propose a fuzzy distance based attribute reduction method on the decision table with numerical attribute value domain. Experiments on data sets show that the proposed method is more efficient than the ones based on Shannon’s entropy on the executed time and the classification accuracy of reduct.

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