Hybrid Method Based on NARX models and Machine Learning for Pattern Recognition

Pedro H. O. Silva; Augusto S. Cerqueira; Erivelton G. Nepomuceno

doi:10.20906/sbai.v1i1.2733

Pedro H. O. Silva Department of Electrical Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora, MG
Augusto S. Cerqueira Department of Electrical Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora, MG
Erivelton G. Nepomuceno Department of Electrical Engineering, Federal University of São João del-Rei (UFSJ), São Joa~o del-Rei, MG

DOI: https://doi.org/10.20906/sbai.v1i1.2733

Keywords: machine learning, system identification, NARX model, feature extraction, dimensionality reduction

Abstract

This work presents a novel technique that integrates the methodologies of machine learning and system identification to solve multiclass problems. Such an approach allows to extract and select sets of representative features with reduced dimensionality, as well as predicts categorical outputs. The efficiency of the method was tested by running case studies investigated in machine learning, obtaining better absolute results when compared with classical classification algorithms.