An effective heuristic for developing hybrid feature selection in high dimensional and low sample size datasets.

ABSTRACT:

Background

High-dimensional datasets with low sample sizes (HDLSS) are pivotal in the fields of biology and bioinformatics. One of core objective of HDLSS is to select most informative features and discarding redundant or irrelevant features. This is particularly crucial in bioinformatics, where accurate feature (gene) selection can lead to breakthroughs in drug development and provide insights into disease diagnostics. Despite its importance, identifying optimal features is still a significant challenge in HDLSS.

Results

To address this challenge, we propose an effective feature selection method that combines gradual permutation filtering with a heuristic tribrid search strategy, specifically tailored for HDLSS contexts. The proposed method considers inter-feature interacti

SUBMITTER: Shin H 

PROVIDER: S-EPMC11670382 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature