  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.
