Minimum decision cost reduct for fuzzy decision-theoretic rough set model

2016 ◽  
Author(s):  
Jingjing Song ◽  
Eric C. C. Tsang ◽  
Degang Chen ◽  
Xibei Yang
Keyword(s):  
Rough Set ◽  
Fuzzy Decision ◽  
Decision Cost

Minimal decision cost reduct in fuzzy decision-theoretic rough set model

2017 ◽  
Vol 126 ◽  
pp. 104-112 ◽  
Author(s):  
Jingjing Song ◽  
Eric C.C. Tsang ◽  
Degang Chen ◽  
Xibei Yang
Keyword(s):  
Rough Set ◽  
Fuzzy Decision ◽  
Decision Cost

scholarly journals Local view based cost-sensitive attribute reduction

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1817-1822
Author(s):  
Jingzheng Li ◽  
Xiangjian Chen ◽  
Pingxin Wang ◽  
Xibei Yang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Data Sets ◽  
Fuzzy Decision ◽  
Single Object ◽  
Local Difference ◽  
Local View ◽  
Sensitive Attribute ◽  
Global And Local ◽  
Decision Cost

In traditional cost-sensitive attribute reduction, the variation of decision cost is referred to as a global difference of costs because the considered decision cost is the variation of sum of decision costs over all objects. However, such reduction does not take the variation of decision costs of each object into account. To solve this problem, a local view based cost-sensitive attribute reduction is introduced. Firstly, through considering the variation of decision costs of single object if the used attributes change, a local difference of costs is presented. Secondly, on the basis of the fuzzy decision-theoretic rough set model, a new significance function is given to measure the importance of attribute. Finally, the experimental results illustrate that by comparing the traditional reduction, the proposed local view can decreases both global and local differences of costs effectively on several UCI data sets.

Fuzzy Attribute Reduction Based on Fuzzy Similarity

2014 ◽  
Vol 533 ◽  
pp. 237-241
Author(s):  
Xiao Jing Liu ◽  
Wei Feng Du ◽  
Xiao Min
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Fuzzy Decision ◽  
Core Content ◽  
Fuzzy Similarity

The measure of the significance of the attribute and attribute reduction is one of the core content of rough set theory. The classical rough set model based on equivalence relation, suitable for dealing with discrete-valued attributes. Fuzzy-rough set theory, integrating fuzzy set and rough set theory together, extending equivalence relation to fuzzy relation, can deal with fuzzy-valued attributes. By analyzing three problems of FRAR which is a fuzzy decision table attribute reduction algorithm having extensive use, this paper proposes a new reduction algorithm which has better overcome the problem, can handle larger fuzzy decision table. Experimental results show that our reduction algorithm is much quicker than the FRAR algorithm.

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