An Efficient Gannet Optimization Algorithm for Feature Selection Based on Sensitivity and Specificity

Overview

This work adapts gannet-inspired search to feature selection, where a compact feature set must still help a classifier detect both positive and negative cases. It considers sensitivity and specificity when evaluating candidate subsets, connecting the optimization process to practical classification needs. The study explores how this nature-inspired search can support more balanced feature selection on benchmark data.