Vibrating Particles System Algorithm for Solving Classification Problems

Abstract

Big data is a term that refers to a set of data that, due to its largeness or complexity, cannot be stored or processed with one of the usual tools or applications for data management, and it has become a prominent word in recent years for the massive development of technology. Almost immediately thereafter, the term “big data mining” emerged, i.e., mining from big data even as an emerging and interconnected field of research. Classification is an important stage in data mining since it helps people make better decisions in a variety of situations, including scientific endeavors, biomedical research, and industrial applications. The probabilistic neural network (PNN) is a commonly used and successful method for handling classification and pattern recognition issues. In this study, the authors proposed to combine the probabilistic neural network (PPN), which is one of the data mining techniques, with the vibrating particles system (VPS), which is one of the metaheuristic algorithms named “VPS-PNN”, to solve classification problems more effectively. The data set is eleven common benchmark medical datasets from the machine-learning library, the suggested method was tested. The suggested VPS-PNN mechanism outperforms the PNN, biogeography-based optimization, enhanced-water cycle algorithm (E-WCA) and the firefly algorithm (FA) in terms of convergence speed and classification accuracy.