Volume 18, No. 6, 2021
A Comparative Evaluation Of Bio-Inspired Optimization Techniques For Feature Selection
A. Ameer Rashed Khan , Dr. S.Shajun Nisha , Dr. M.Mohamed Sathik
Abstract
Feature selection is an effective approach to reduce the number of features of data, which enhances the performance of classification in machine learning. In this paper, we compare feature selection optimization algorithms to reduce the number of the selected features while enhancing the accuracy. Bio-Inspired Optimization Algorithm is based on biological evolution of nature or from inspirational biological environment. It is an emerging approach used to develop new and robust competing techniques. Optimization means making things better or most effective use of situation. For solving learning and data analysis problems this techniques are used for better performance. In Medical data analysis, optimization techniques and hybrid Bio- Inspired techniques are merged, it is used mainly in Machine learning and Artificial intelligence. In this paper various bio inspired optimization algorithms like Ant Colony Optimization (ACO), Bat Optimization (BAT), Particle Swarm Algorithm (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Slime Mould Optimization (SMA) has been applied to various classification datasets from the UCI machine learning repository.
Pages: 4241-4256
Keywords: Bio-Inspired, Optimization, Feature Selection, Ant Colony Optimization (ACO), Bat Optimization (BAT), Particle Swarm Algorithm (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Slime Mould Optimization (SMA).