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
traditional classification algorithms
