SVM-RFE: selection and visualization of the most relevant features through non-linear kernels.

ABSTRACT:

Background

Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance of predictor variables. Creating predictor models based on only the most relevant variables is essential in biomedical research. Currently, substantial work has been done to allow assessment of variable importance in SVM models but this work has focused on SVM implemented with linear kernels. The power of SVM as a prediction model is associated with the flexibility generated by use of non-linear kernels. Moreover, SVM has been extended to model survival outcomes. This paper extends the Re

SUBMITTER: Sanz H 

PROVIDER: S-EPMC6245920 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature