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.

Rough Set Classification Algorithms Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3119-3122
Author(s):  
Yan Xue Dong ◽  
Fu Hai Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Texture Classification ◽  
Texture Feature ◽  
Decision Table ◽  
Classification Algorithms ◽  
Reduction Algorithm ◽  
Discrete Method ◽  
Knowledge Reduction

The basic theory of rough set is given and a method for texture classification is proposed. According to the GCLM theory, texture feature is extracted and generate 32 feature vectors to form a decision table, find a minimum set of rules for classification after attribute discretization and knowledge reduction, experimental results show that using rough set theory in texture classification, accompanied by appropriate discrete method and reduction algorithm can get better classification results

scholarly journals Parallel Attribute Reduction Algorithm for Complex Heterogeneous Data Using MapReduce

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tengfei Zhang ◽  
Fumin Ma ◽  
Jie Cao ◽  
Chen Peng ◽  
Dong Yue
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Heterogeneous Data ◽  
Reduction Algorithm ◽  
Numerical Attributes ◽  
Reduction Efficiency ◽  
Speed Up

Parallel attribute reduction is one of the most important topics in current research on rough set theory. Although some parallel algorithms were well documented, most of them are still faced with some challenges for effectively dealing with the complex heterogeneous data including categorical and numerical attributes. Aiming at this problem, a novel attribute reduction algorithm based on neighborhood multigranulation rough sets was developed to process the massive heterogeneous data in the parallel way. The MapReduce-based parallelization method for attribute reduction was proposed in the framework of neighborhood multigranulation rough sets. To improve the reduction efficiency, the hashing Map/Reduce functions were designed to speed up the positive region calculation. Thereafter, a quick parallel attribute reduction algorithm using MapReduce was developed. The effectiveness and superiority of this parallel algorithm were demonstrated by theoretical analysis and comparison experiments.

Attribute Reduction Algorithm for Fuzzy Decision Table with Membership Degrees and Weights

2012 ◽  
Vol 198-199 ◽  
pp. 1367-1371
Author(s):  
Hao Dong Zhu ◽  
Hong Chan Li
Keyword(s):  
Rough Set ◽  
Decision Procedure ◽  
Attribute Reduction ◽  
Decision Table ◽  
Membership Degree ◽  
Reduction Algorithm ◽  
Fuzzy Decision ◽  
Definite Form

The classical rough set can not show the fuzziness and the importance of objects in decision procedure because it uses definite form to express each object. In order to solve this problem, this paper firstly introduces a special decision table in which each object has a membership degree to show its fuzziness and has been assigned a weight to show its importance in decision procedure. And then, the special decision table is studied and the relevant rough set model is provided. In the meantime, relevant definitions and theorems are proposed. On the above basis, an attribute reduction algorithm is presented. Finally, feasibility of the relevant rough set model and the presented attribute reduction algorithm are verified by an example.

Binary Resolution Matrix Based Incremental Attribute Reduction for Fault Diagnosis

2016 ◽  
Vol 693 ◽  
pp. 1346-1349
Author(s):  
Xiao Yu Chen ◽  
Wen Liao Du ◽  
An Sheng Li ◽  
Kun Li ◽  
Chun Hua Qian
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Rotating Machinery ◽  
Reduction Algorithm

Rough set theory is a useful tool for attribute reduction of fault diagnosis for rotating machinery, but cannot be efficiently used to sample increased areas. Aiming at the problem of incremental attribute reduction, a novel attribute reduction algorithm was put forward based on the binary resolution matrix for the two updating situations and the algorithm had a low space complex. Finally, with the fault diagnosis experiments of the bearing, the attribute reduction method was proved to be correct.

One Fusion Algorithm of Equipment Fault Prediction Based on Rough Set Theory and Grey Model

2014 ◽  
Vol 687-691 ◽  
pp. 1377-1379
Author(s):  
Zhen Yu Song ◽  
Guang Yi Zhang ◽  
Yan Qin Su
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Fault Prediction ◽  
Decision Table ◽  
Grey Theory ◽  
Fusion Algorithm ◽  
Equipment Reliability ◽  
Radio Equipment

Rough set theory and grey theory have the same advantage of processing inaccuracy data, so one fusion algorithm based on them is proposed. The attribute reduction algorithm of rough set theory can reduce the decision table of fault diagnosis, and grey theory can predict the fault based on the new reduced decision table. Then it is verified in some aero radio equipment, and the results indicate that the accuracy of fault prediction is quite higher, which provides the foundation to improve the equipment reliability and maintainability.

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