Feature Selection of Atmospheric Corrosion Data Based on SVM-RFE Method

Xintao Qiu, Dongmei Fu, Zhenduo Fu

Abstract


As for the small samples typically in atmospheric corrosion of metals, this paper introduces a new methods of dimension reduction to conduct data processing and modeling. Through combining the support vector machine(SVM) and recursive feature elimination(RFE), SVM-RFE algorithm is proposed to select features in corrosion data. This method provides a new feature selection and modeling method for atmospheric corrosion data with high accuracy, which pledges that the remaining feature subset is optimal. The validation of simulation experiment data shows that the proposed feature selection method can be effectively applied to the research work on analyzing and modeling the data of metal atmospheric corrosion, by which the dimension reduction during processing and modeling atmospheric corrosion data can be achieved.

 


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