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

TL;DR

This paper introduces a hybrid approach combining NARX models and machine learning for improved pattern recognition, focusing on feature selection and categorical prediction, demonstrating superior results over classical classifiers.

Contribution

It presents a novel integration of NARX models with machine learning techniques for multiclass pattern recognition, emphasizing feature reduction and prediction accuracy.

Findings

Achieved better results than classical classifiers in case studies.

Effectively reduces feature dimensionality.

Improves categorical prediction accuracy.

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.