Hybrid Method Based on NARX models and Machine Learning for Pattern Recognition
Keywords: machine learning, system identification, NARX model, feature extraction, dimensionality reduction
AbstractThis 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.