SVM Regression Modeling Based on Properties of Engineering Materials with PLS Feature Extraction

Ya Rong Wu; Hua Ping Li; Xu Sheng Gan

doi:10.4028/www.scientific.net/AMR.848.122

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SVM Regression Modeling Based on Properties of Engineering Materials with PLS Feature Extraction
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 848SVM Regression Modeling Based on Properties of...

SVM Regression Modeling Based on Properties of Engineering Materials with PLS Feature Extraction

Abstract:

A combination regression algorithm of PLS-SVM is proposed for the properties of engineering materials. The algorithm first extracts the feature from the primal sample sets by PLS, and then carries out SVM regression estimation using the new sample sets attained. Thus the algorithm will have the ability of extracting feature. The simulation results indicate that the new combined SVM regression algorithm can effectively solve the regression estimation problem including dimensions reduction, noise elimination and multiple correlations between independent variables. The algorithm has good and wide application foreground in research of properties of engineering materials.

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Periodical:

Advanced Materials Research (Volume 848)

Pages:

122-125

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.848.122

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Online since:

November 2013

Authors:

Ya Rong Wu, Hua Ping Li, Xu Sheng Gan

Keywords:

Feature Extraction, Partial Least Square, Principal Component, Support Vector Machine (SVM)

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