Neighborhood-based Multigranulation Rough Set in Incomplete Grey Fuzzy Information System

2014 ◽  
Vol 11 (12) ◽  
pp. 4439-4451
Author(s):  
Tianqing Wang
Keyword(s):  
Information System ◽  
Rough Set ◽  
Fuzzy Information ◽  
Multigranulation Rough Set

scholarly journals Relative Reduction of Neighborhood-Covering Pessimistic Multigranulation Rough Set Based on Evidence Theory

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 334 ◽  
Author(s):  
Xiaoying You ◽  
Jinjin Li ◽  
Hongkun Wang
Keyword(s):  
Information System ◽  
Rough Set ◽  
Evidence Theory ◽  
Belief Function ◽  
Relative Reduction ◽  
Reduction Theory ◽  
Research Topics ◽  
Knowledge Reduction ◽  
Multigranulation Rough Set ◽  
Lower And Upper Approximations

Relative reduction of multiple neighborhood-covering with multigranulation rough set has been one of the hot research topics in knowledge reduction theory. In this paper, we explore the relative reduction of covering information system by combining the neighborhood-covering pessimistic multigranulation rough set with evidence theory. First, the lower and upper approximations of multigranulation rough set in neighborhood-covering information systems are introduced based on the concept of neighborhood of objects. Second, the belief and plausibility functions from evidence theory are employed to characterize the approximations of neighborhood-covering multigranulation rough set. Then the relative reduction of neighborhood-covering information system is investigated by using the belief and plausibility functions. Finally, an algorithm for computing a relative reduction of neighborhood-covering pessimistic multigranulation rough set is proposed according to the significance of coverings defined by the belief function, and its validity is examined by a practical example.

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

Filomat ◽  
2020 ◽  
Vol 34 (7) ◽  
pp. 2253-2272
Author(s):  
Zhanglin Xian ◽  
Jinkun Chen ◽  
Peiqiu Yu
Keyword(s):  
Information System ◽  
Rough Set ◽  
Rough Sets ◽  
Original Method ◽  
Complex Data ◽  
Dynamic Information ◽  
Useful Knowledge ◽  
Multigranulation Rough Set ◽  
High Computational Complexity ◽  
Relation Matrix

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.

Rough Set on Two Universal Sets Based on Multigranulation

2014 ◽  
pp. 159-179 ◽  
Author(s):  
D. P. Acharjya ◽  
Mary A. Geetha
Keyword(s):  
Information System ◽  
Rough Set ◽  
Rough Sets ◽  
Real Life ◽  
Equivalence Relations ◽  
Topological Characterization ◽  
Life Problems ◽  
Multigranulation Rough Set ◽  
Algebraic Properties ◽  
The Universe

The fundamental concept of crisp set has been extended in many directions in the recent past. The notion of rough set by Pawlak is noteworthy among them. The rough set philosophy is based on the concept that there is some information associated with each object of the universe. There is a need to classify objects of the universe based on the indiscernibility relation among them. In the view of granular computing, rough set model is researched by single granulation. It has been extended to multigranular rough set model in which the set approximations are defined by using multiple equivalence relations on the universe simultaneously. However, in many real life scenarios, an information system establishes the relation with different universes. This gave the extension of multigranulation rough set on single universal set to multigranulation rough set on two universal sets. This chapter defines multigranulation rough set for two universal sets U and V. In addition, the algebraic properties, measures of uncertainty and topological characterization that are interesting in the theory of multigranular rough sets are studied. This helps in describing and solving real life problems more accurately.

scholarly journals A Novel Decision-Making Approach to Fund Investments Based on Multigranulation Rough Set

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Xima Yue ◽  
Xiang Su
Keyword(s):  
Decision Making ◽  
Information System ◽  
Rough Set ◽  
Investment Decision ◽  
Decision Rules ◽  
Investment Project ◽  
Point Of View ◽  
New Approach ◽  
Multigranulation Rough Set ◽  
Fund Investment

Fund investment is a hot issue in today’s society. How to choose a project for investment is affected by many factors. In view of this problem, this paper starts from the granular computing point of view and combines the multigranulation rough set decision-making method to construct a fund investment decision information system; then, the fund investment decision information system is reduced under different thresholds, and the decision rules are extracted through reduction. And from the aspects of decision accuracy and rule accuracy, the rules are analyzed. Finally, decision rules are used to give the decision of the fund investment project. This study provides a new approach to fund management.

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