An efficient hybrid filter-wrapper method based on improved Harris Hawks optimization for feature selection.

ABSTRACT:

Introduction

High-dimensional datasets often contain an abundance of features, many of which are irrelevant to the subject of interest. This issue is compounded by the frequently low number of samples and imbalanced class samples. These factors can negatively impact the performance of classification algorithms, necessitating feature selection before classification. The primary objective of feature selection algorithms is to identify a minimal subset of features that enables accurate classification.

Methods

In this paper, we propose a two-stage hybrid method for the optimal selection of relevant features. In the first stage, a filter method is employed to assign weights to the features, facilitating the removal of redundant and irrelevant features and reducing the computation

SUBMITTER: Pirgazi J 

PROVIDER: S-EPMC12008497 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature