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. Resumo: O presente trabalho apresenta uma nova técnica que integra as metodologias de aprendizado de máquinas e identificação de sistemas na solução de problemas multiclasses. A abordagem permite extrair e selecionar conjuntos de características representativas com dimensionalidade reduzida, da mesma forma que prediz saídas categóricas. A eficiência do método é testada pela aplicação em estudos de casos estudados no aprendizado de máquina, obtendo melhores resultados absolutos em comparação aos algoritmos clássicos de classificação.
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