Frog-Snake prey-predation Relationship Optimization (FSRO) : A novel nature-inspired metaheuristic algorithm for feature selection

TL;DR

The paper introduces FSRO, a new nature-inspired metaheuristic algorithm based on frog-snake prey-predation dynamics, designed for feature selection in machine learning, demonstrating improved accuracy and data reduction.

Contribution

It presents the FSRO algorithm, integrating prey-predation behavior and evolutionary game theory for effective feature selection in discrete optimization.

Findings

FSRO outperforms comparison algorithms in accuracy and fitness value.

Dynamic search control via evolutionary game theory enhances performance.

The algorithm balances exploration and exploitation effectively.

Abstract

Swarm intelligence algorithms have traditionally been designed for continuous optimization problems, and these algorithms have been modified and extended for application to discrete optimization problems. Notably, their application in feature selection for machine learning has demonstrated improvements in model accuracy, reduction of unnecessary data, and decreased computational time. This study proposes the Frog-Snake prey-predation Relationship Optimization (FSRO) algorithm, inspired by the prey-predation relationship between frogs and snakes for application to discrete optimization problems. The algorithm models three stages of a snake's foraging behavior "search", "approach", and "capture" as well as the frog's characteristic behavior of staying still to attract and then escaping. Furthermore, the introduction of the concept of evolutionary game theory enables dynamic control of the search process. The proposed algorithm conducts computational experiments on feature selection using 26 types of machine learning datasets to analyze its performance and identify improvements. In computer experiments, the proposed algorithm showed better performance than the comparison algorithms in terms of the best and standard deviation of fitness value and Accuracy. It was also proved that dynamic search control by evolutionary game theory is an effective method, and the proposed algorithm has the ability of a well-balanced search, achieving the two objectives of improving accuracy and reducing data.