A novel attribute reduction approach for multi-label data based on rough set theory

2016 ◽  
Vol 367-368 ◽  
pp. 827-847 ◽  
Author(s):  
Hua Li ◽  
Deyu Li ◽  
Yanhui Zhai ◽  
Suge Wang ◽  
Jing Zhang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Label Data ◽  
Reduction Approach

scholarly journals Attribute Reduction Based on Consistent Covering Rough Set and Its Application

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jianchuan Bai ◽  
Kewen Xia ◽  
Yongliang Lin ◽  
Panpan Wu
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Processing Step ◽  
Important Processing ◽  
Continuous Attribute ◽  
Covering Rough Set

As an important processing step for rough set theory, attribute reduction aims at eliminating data redundancy and drawing useful information. Covering rough set, as a generalization of classical rough set theory, has attracted wide attention on both theory and application. By using the covering rough set, the process of continuous attribute discretization can be avoided. Firstly, this paper focuses on consistent covering rough set and reviews some basic concepts in consistent covering rough set theory. Then, we establish the model of attribute reduction and elaborate the steps of attribute reduction based on consistent covering rough set. Finally, we apply the studied method to actual lagging data. It can be proved that our method is feasible and the reduction results are recognized by Least Squares Support Vector Machine (LS-SVM) and Relevance Vector Machine (RVM). Furthermore, the recognition results are consistent with the actual test results of a gas well, which verifies the effectiveness and efficiency of the presented method.

The Incremental Knowledge Acquisition Based on Hash Algorithm

2016 ◽  
Vol 24 (03) ◽  
pp. 347-366 ◽  
Author(s):  
Qing-Hua Zhang ◽  
Long-Yang Yao ◽  
Guan-Sheng Zhang ◽  
Yu-Ke Xin
Keyword(s):  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Algorithm Analysis ◽  
Data Sets ◽  
Hash Algorithm ◽  
Acquisition Method

In this paper, a new incremental knowledge acquisition method is proposed based on rough set theory, decision tree and granular computing. In order to effectively process dynamic data, describing the data by rough set theory, computing equivalence classes and calculating positive region with hash algorithm are analyzed respectively at first. Then, attribute reduction, value reduction and the extraction of rule set by hash algorithm are completed efficiently. Finally, for each new additional data, the incremental knowledge acquisition method is proposed and used to update the original rules. Both algorithm analysis and experiments show that for processing the dynamic information systems, compared with the traditional algorithms and the incremental knowledge acquisition algorithms based on granular computing, the time complexity of the proposed algorithm is lower due to the efficiency of hash algorithm and also this algorithm is more effective when it is used to deal with the huge data sets.

scholarly journals Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

2019 ◽  
Vol 11 (17) ◽  
pp. 4513 ◽  
Author(s):  
Xiaoqing Li ◽  
Qingquan Jiang ◽  
Maxwell K. Hsu ◽  
Qinglan Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Assessment Model ◽  
Software Project ◽  
Risk Assessment Model ◽  
Backpropagation Neural Network ◽  
Model Based ◽  
Sample Data

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.

Surface Roughness Prediction of Engineering Ceramics Electro-Spark Machining Based on Rough Set Neural Network

2010 ◽  
Vol 43 ◽  
pp. 269-273
Author(s):  
Xue Li ◽  
He Wang ◽  
Shu Fen Chen
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Processing Parameters ◽  
Relational Model ◽  
Engineering Ceramics ◽  
Roughness Prediction

To solve the problem of difficulty in establishing the mathematical model between process parameters and surface quality in the process of engineering ceramics electro-spark machining, a neural network relational model based on rough set theory is presented. By processing attribute reduction from data sample utilizing rough set theory, defects like bulkiness of neural network structure and difficult convergence etc are aovided when input dimensions is high. A prediction model that a surface roughness varies in accordance with processing parameters in application of well structured neural network rough set is established. Study result shows that utilizing this model can precisely predict surface roughness under the given conditions with little error which proves the reliability of this model.

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.

The Application of Data Mining in the Civil Aviation Accident Analysis

2012 ◽  
Vol 241-244 ◽  
pp. 3000-3004
Author(s):  
Dai Wu Zhu ◽  
Yin Ni
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Passive State ◽  
Civil Aviation ◽  
Accident Analysis ◽  
Analysis Method ◽  
Accident Prediction

At present, our analysis of the aviation accident mainly limited to the methods of mathematical statistics, the analysis method means of a single, and in a passive state, so the accident prediction is poor. This paper, basis on the rough set theory in data mining and preferential information ,we improve the rough set attribute reduction algorithm, and applied to civil aviation accident analysis to indentify the potential law of accident.

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