Attribute Reduction Based on Consistent Covering Rough Set and Its Application
| Autor: | Yongliang Lin, Kewen Xia, Jianchuan Bai, Panpan Wu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Multidisciplinary Article Subject General Computer Science Discretization Generalization Dominance-based rough set approach 02 engineering and technology computer.software_genre lcsh:QA75.5-76.95 Relevance vector machine Reduction (complexity) 020901 industrial engineering & automation Data redundancy Least squares support vector machine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Data mining Rough set computer Mathematics |
| Zdroj: | Complexity, Vol 2017 (2017) |
| ISSN: | 1099-0526 1076-2787 |
| DOI: | 10.1155/2017/8986917 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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