Stable Iterative Variable Selection.

ABSTRACT:

Motivation

The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space.

Results

Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using S

SUBMITTER: Mahmoudian M 

PROVIDER: S-EPMC8665768 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature