Attribute reduction based on misclassification cost in variable precision rough set model

2019 ◽  
Vol 37 (4) ◽  
pp. 5129-5142
Author(s):  
Jingjing Yang ◽  
Qinghua Zhang ◽  
Qin Xie
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Misclassification Cost ◽  
Variable Precision ◽  
Variable Precision Rough Set

The Research of Constructing Rough Concept Lattices Model

2011 ◽  
Vol 63-64 ◽  
pp. 664-667
Author(s):  
Hong Sheng Xu ◽  
Ting Zhong Wang
Keyword(s):  
Rough Set ◽  
Rough Set Theory ◽  
Concept Lattice ◽  
Attribute Reduction ◽  
Formal Concept ◽  
Concept Lattices ◽  
Variable Precision ◽  
Whole Process ◽  
Variable Precision Rough Set ◽  
Lattice Construction

Formal concept lattices and rough set theory are two kinds of complementary mathematical tools for data analysis and data processing. The algorithm of concept lattice reduction based on variable precision rough set is proposed by combining the algorithms of β-upper and lower distribution reduction in variable precision rough set. The traditional algorithms aboutβvalue select algorithm, attribute reduction based on discernibility matrix and extraction rule in VPRS are discussed, there are defects in these traditional algorithms which are improved. Finally, the generation system of concept lattice based on variable precision rough set is designed to verify the validity of the improved algorithm and a case demonstrates the whole process of concept lattice construction.

ATTRIBUTE REDUCTION IN VARIABLE PRECISION ROUGH SET MODEL

2006 ◽  
Vol 14 (04) ◽  
pp. 461-479 ◽  
Author(s):  
MASAHIRO INUIGUCHI
Keyword(s):  
Boolean Function ◽  
Rough Set ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Variable Precision ◽  
Variable Precision Rough Set ◽  
Variable Precision Rough Sets

In this paper, attribute reduction in variable precision rough set model is discussed. Several kinds of reducts preserving some of lower approximations, upper approximations, boundary regions and the unpredictable region are discussed. Relations among those kinds of reducts are investigated. As a basis for reduct computation, Boolean function representations of the preservation of lower approximations, upper approximations, boundary regions and the unpredictable region are discussed. Throughout this paper, the great difference between the analysis using variable precision rough sets and the classical rough set analysis is emphasized.

scholarly journals Structure-Based Attribute Reduction in Variable Precision Rough Set Models

2006 ◽  
Vol 10 (5) ◽  
pp. 657-665 ◽  
Author(s):  
Masahiro Inuiguchi ◽  
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Computational Approaches ◽  
Structure Preserving ◽  
Variable Precision ◽  
Paper Structure ◽  
Variable Precision Rough Set

In this paper, structure-enhancing approaches to attribute reduction are proposed. Ten kinds of meaningful reducts are defined. The relations among them are clarified. Moreover their relations to reducts by structure-preserving approaches are also investigated. A few computational approaches to the proposed reducts are briefly described.

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