Measures for evaluating the decision performance of a decision table in rough set theory

2008 ◽  
Vol 178 (1) ◽  
pp. 181-202 ◽  
Author(s):  
Yuhua Qian ◽  
Jiye Liang ◽  
Deyu Li ◽  
Haiyun Zhang ◽  
Chuangyin Dang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Table ◽  
Decision Performance

Application of Rough Set Theory to Rule‐type Knowledge Discovery from Field Inspection Data on Highway Bridges

2017 ◽  
Author(s):  
Ayaho Miyamoto
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Highway Bridges ◽  
Decision Table ◽  
Field Inspection ◽  
Rule Type ◽  
Inspection Data ◽  
Contradictory Data

This paper describes an acquisitive method of rule‐type knowledge from the field inspection data on highway bridges. The proposed method is enhanced by introducing an improvement to a traditional data mining technique, i.e. applying the rough set theory to the traditional decision table reduction method. The new rough set theory approach helps in cases of exceptional and contradictory data, which in the traditional decision table reduction method are simply removed from analyses. Instead of automatically removing all apparently contradictory data cases, the proposed method determines whether the data really is contradictory and therefore must be removed or not. The method has been tested with real data on bridge members including girders and filled joints in bridges owned and managed by a highway corporation in Japan. There are, however, numerous inconsistent data in field data. A new method is therefore proposed to solve the problem of data loss. The new method reveals some generally unrecognized decision rules in addition to generally accepted knowledge. Finally, a computer program is developed to perform calculation routines, and some field inspection data on highway bridges is used to show the applicability of the proposed method.

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

Rules Mining of Power System Transient Stability Assessment Based on Clustering Analysis and Rough Set Theory

2014 ◽  
Vol 521 ◽  
pp. 418-422 ◽  
Author(s):  
Yan Xu ◽  
Xin Chen
Keyword(s):  
Power System ◽  
Set Theory ◽  
Rough Set ◽  
Clustering Analysis ◽  
Transient Stability ◽  
Rough Set Theory ◽  
Decision Table ◽  
Final Decision ◽  
Stability Assessment ◽  
Decision Attributes

Transient Stability Assessment (TSA) aims at assessing stability of power system operation state quickly. This paper introduces rough set theory and clustering analysis to assess power system transient stability. At first, the stability operation parameters and fault places are taken as feature attributes based on the trait of power system transient ability. K-means algorithm is used to make continuous attributes among feature attributes discrete. Then feature attributes and stability types are taken as conditional attributes and decision attributes respectively. Initial decision table is established. Finally, rough set theory is used to form final decision table and rules of TSA are obtained. The IEEE 9-Bus system is employed to demonstrate the validity of the proposed approach.

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.

Feature Extraction from Engine's Data Based on Rough Sets Theory

2011 ◽  
Vol 120 ◽  
pp. 410-413
Author(s):  
Feng Wang ◽  
Li Xin Jia
Keyword(s):  
Feature Extraction ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Technical Condition ◽  
Decision Table ◽  
Discernibility Matrix ◽  
Rough Sets Theory ◽  
Sets Theory

The speed signal of engine contains abundant information. This paper introduces rough set theory for feature extraction from engine's speed signals, and proposes a method of mining useful information from a mass of data. The result shows that the discernibility matrix algorithm can be used to reduce attributes in decision table and eliminate unnecessary attributes, efficiently extracted the features for evaluating the technical condition of engine.

scholarly journals Some algorithms related to consistent decision table

2017 ◽  
Vol 33 (2) ◽  
pp. 131-142
Author(s):  
Quang Minh Hoang ◽  
Vu Duc Thi ◽  
Nguyen Ngoc San
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Polynomial Time ◽  
Time Complexity ◽  
Rough Set Theory ◽  
Decision Rules ◽  
The Other ◽  
Decision Table ◽  
Mathematical Tool ◽  
The Given

Rough set theory is useful mathematical tool developed to deal with vagueness and uncertainty. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. Rough set theory is also the most popular for generating decision rules from decision table. In this paper, we propose an algorithm finding object reduct of consistent decsion table. On the other hand, we also show an algorithm to find some attribute reducts and the correctness of our algorithms is proof-theoretically. These our algorithms have polynomial time complexity. Our finding object reduct helps other algorithms of finding attribute reducts become more effectively, especially as working with huge consistent decision table.

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.

A Discretization Method of Continuous Attributes Based on Rough Set

2014 ◽  
Vol 556-562 ◽  
pp. 3711-3713
Author(s):  
Xiao Kang Tang ◽  
Xue Zhi Zhang ◽  
Qiong Zou ◽  
You Guo Wei ◽  
Cheng Jun Cao
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Conditional Entropy ◽  
Decision Table ◽  
Representation System ◽  
Specific Data ◽  
Discretization Algorithm ◽  
Discretization Technique ◽  
Knowledge Representation System

when the rough set be used to deal with Knowledge representation system, the data in decision table should be expressed in discrete data, if some conditions or decision attribute is continuous value, which should be discrete Before process.Discretization is not specific data processing only by rough set theory , people have conducted extensive research on discretization problem before the rough set theory put forward , and Made a lot of progress ,but the discretization technique is can not be completely in common used in every subject, different areas have their own unique requirements and handling .This paper proposes a discretization algorithm based on regular conditional entropy.

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