A Novel Hybrid Model for Information Processing Basing on Rough Sets and Fuzzy SVM

Rough set theory (RST) is a new effective tool in dealing with vagueness and uncertainty information from a large number of data. Fuzzy support vector machine (FSVM) has become the focus of research in machine learning. And it greatly improves the capabilities of fault-tolerance and generalization o...

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Bibliographic Details
Main Authors: Guang-ming Xian, Bi-qing Zeng
Format: Conference Proceeding
Language:English
Subjects:
Fault tolerance
Focusing
Fuzzy sets
Fuzzy support vector machine
Image quality
Information processing
Machine learning
Rough set
Rough sets
Set theory
Support vector machines
Uncertainty
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Summary:Rough set theory (RST) is a new effective tool in dealing with vagueness and uncertainty information from a large number of data. Fuzzy support vector machine (FSVM) has become the focus of research in machine learning. And it greatly improves the capabilities of fault-tolerance and generalization of standard support vector machine. The hybrid model of RS-FSVM inherits the merits of RS and FSVM, and is applied into fused image quality evaluation in this paper. RST is used as preprocessing step to improve the performances of FSVM. A large number of experimental results show that when the number training samples are enough RS-SVM can achieve higher precision of classification than methods of FSVM and SVM.
DOI:10.1109/MUE.2008.68