An Ensemble Method for Data Classification Using State-of-the-art Methodologies

Abstract

Over the past few decades, classification has consistently posed a significant computational challenge. This study presents an innovative ensemble classification model designed for data classification, drawing inspiration from Radial Basis Function, Extreme Learning Machine, Functional Linked Artificial Neural Network, and Artificial Neural Network. The study involved experimenting with various combinations of ensemble methods to construct an ensemble classifier. Remarkably, implementing the ensemble model using Radial Basis Function Network (RBFN), Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Functional Linked Artificial Neural Network (FLANN) yielded superior results when tested on benchmark datasets. The accuracy range of the ensemble method varies from 80% to 98% which is a good performance considering the diverge data sets used.