HDSI: High dimensional selection with interactions algorithm on feature selection and testing.

ABSTRACT: Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the ap

SUBMITTER: Jain R 

PROVIDER: S-EPMC7886179 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature