Improving PSO Global Method for Feature Selection According to Iterations Global Search and Chaotic Theory
This work addresses feature selection for machine learning and data mining, but it is incremental as it builds on existing PSO methods.
The paper tackled the problem of feature selection to reduce computational complexity and improve classification accuracy by proposing an improved particle swarm optimization method with chaotic theory, achieving superior performance compared to state-of-the-art methods on most real-world datasets.
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.