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.

Research on Fault Diagnosis of Forklift’s Hydraulic System Based on Rough Set Theory

2012 ◽  
Vol 524-527 ◽  
pp. 819-823
Author(s):  
Xin Ping Su ◽  
Guang Kun Nie ◽  
Wei Xin Fan
Keyword(s):  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Hydraulic System ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Train Of Thought ◽  
Fault Information

An approach of forklift’s fault diagnostic knowledge acquisition and discrete date based on rough set theory was put forward, according to the rough set theory in fault diagnosis of fault tolerance, the use of rough set theory in fault knowledge attribute reduction and value reduction, as in incomplete fault information of forklift hydraulic system fault diagnosis provides a train of thought. The inferential strategy and process of fault diagnosis of hydraulic system for forklift were described. Examples show that the proposed approach is very effective.

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.

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.

A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

2013 ◽  
Vol 17 (3) ◽  
pp. 371-376 ◽  
Author(s):  
Yasuo Kudo ◽  
◽  
Tetsuya Murai ◽  
Keyword(s):  
Data Mining ◽  
Parallel Computation ◽  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Computation Method ◽  
Decision Tables

In this paper, we propose a parallel computation framework for a heuristic attribute reduction method. Attribute reduction is a key technique to use rough set theory as a tool in data mining. The authors have previously proposed a heuristic attribute reduction method to compute as many relative reducts as possible from a given dataset with numerous attributes. We parallelize our method by using open multiprocessing. We also evaluate the performance of a parallelized attribute reduction method by experiments.

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