A Binary Chaotic Transient Search Optimization Algorithm for Enhancing Feature Selection

Overview

This paper adapts transient search optimization to the binary choices used in feature selection. Chaotic sequences guide the search across candidate feature subsets, helping the method explore more widely and avoid redundant inputs. Evaluated on 12 UCI datasets, the approach is compared with established methods using classification performance, convergence, and diversity. It offers a focused way to select compact, informative feature sets.