Analysis of Meta-Heuristic Feature Selection Techniques on classifier performance with specific reference to psychiatric disorder

Abstract

Optimization plays an important role in solving complex computational problems. Meta-Heuristic  approaches work as an optimization technique. In any search space, these approaches play an excellent role in local as well as global search. Nature-inspired approaches, especially population-based ones, play a role in solving the problem. In the past decade, many nature-inspired population-based methods have been explored by researchers to facilitate computational intelligence. These methods are based on insects, birds, animals, sea creatures, etc. This research focuses on the use of Meta-Heuristic methods for the feature selection. A better optimization approach must be introduced to reduce the computational load, depending on the problem size and complexity. The correct feature set must be chosen for the diagnostic system to operate effectively. Here, population-based Meta-Heuristic optimization strategies have been used to pick the features. By choosing the best feature set, the Butterfly Optimization Algorithm (BOA) with the Enhanced Lion Optimization Algorithm (ELOA) approach would reduce classifier overhead. The results clearly demonstrate that the combined strategy has higher performance outcomes when compared to other optimization strategies.