A Wrapper Feature Subset Selection Method Based on Randomized Search and Multilayer Structure.

ABSTRACT: The identification of discriminative features from information-rich data with the goal of clinical diagnosis is crucial in the field of biomedical science. In this context, many machine-learning techniques have been widely applied and achieved remarkable results. However, disease, especially cancer, is often caused by a group of features with complex interactions. Unlike traditional feature selection methods, which only focused on finding single discriminative features, a multilayer feature subset selection method (MLFSSM), which employs randomized search and multilayer structure to select a discriminative subset, is proposed herein. In each level of this method, many feature subsets are generated to assure the diversity of the combinations, and the weights of features are evaluated on the

SUBMITTER: Mao Y 

PROVIDER: S-EPMC6885241 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature