Hybrid Method Based on NARX models and Machine Learning for Pattern Recognition
This incremental method addresses pattern recognition problems for researchers and practitioners in machine learning.
The authors tackled multiclass pattern recognition by integrating machine learning with system identification to extract and select features with reduced dimensionality, achieving better absolute results compared to classical classification algorithms in case studies.
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.