Hierarchical Classification Using Evolutionary Strategy


  • Helyane Bronoski Borges Federal University of Technology – Paraná (UTFPR), Address Doutor Washington Subtil Chueire, 330 - Jardim Carvalho, Ponta Grossa, 84017-220, Parana, Brazil
  • Julio Cesar Nievola Pontifical Catholic University of Paraná (PUCPR), Address Imaculada Conceição, 1155 - Prado Velho, Curitiba, 80215-901, Parana, Brazil
  • Simone Nasser Matos Federal University of Technology – Paraná (UTFPR), Address Doutor Washington Subtil Chueire, 330 - Jardim Carvalho, Ponta Grossa, 84017-220, Parana, Brazil


Hierarchical Classification, Evolutionary Strategy, Classifier


Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents experimenters using the algorithm for hierarchical classification called Hierarchical Classification using Evolutionary Strategy (HC-ES). It was tested in eight datasets the G-Protein-Coupled Receptor (GPCR) and Enzyme Commission Codes (EC). The results are compared with other hierarchical classifier using the distance and hF-Measure.


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How to Cite

Borges, H. B. ., Nievola, J. C. ., & Matos, S. N. . (2020). Hierarchical Classification Using Evolutionary Strategy. American Scientific Research Journal for Engineering, Technology, and Sciences, 68(1), 40–49. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/5879