Improving PSO Global Method for Feature Selection According to Iterations Global Search and Chaotic Theory

TL;DR

This paper introduces an improved particle swarm optimization method that leverages chaos theory and iteration-based feature evaluation to enhance feature selection accuracy and efficiency in machine learning tasks.

Contribution

It proposes a novel initialization strategy using chaos theory and a new feature size formula, improving search space exploration and feature selection performance.

Findings

Outperforms state-of-the-art methods on real-world datasets

Enhances exploration with chaos-based initialization

Reduces feature set size while maintaining accuracy

Abstract

Making a simple model by choosing a limited number of features with the purpose of reducing the computational complexity of the algorithms involved in classification is one of the main issues in machine learning and data mining. The aim of Feature Selection (FS) is to reduce the number of redundant and irrelevant features and improve the accuracy of classification in a data set. We propose an efficient ISPSO-GLOBAL (Improved Seeding Particle Swarm Optimization GLOBAL) method which investigates the specified iterations to produce prominent features and store them in storage list. The goal is to find informative features based on its iteration frequency with favorable fitness for the next generation and high exploration. Our method exploits of a new initialization strategy in PSO which improves space search and utilizes chaos theory to enhance the population initialization, then we offer a new formula to determine the features size used in proposed method. Our experiments with real-world data sets show that the performance of the ISPSO-GLOBAL is superior comparing with state-of-the-art methods in most of the data sets.