Analysis of the Threshold Variation of the FlexCon-C Algorithm for Semi-supervised Learning

  • Arthur C. Gorgônio UFRN
  • Cainan T. Alves UFRN
  • Amarildo J. F. Lucena UFRN
  • Flavius L. Gorgônio UFRN
  • Karliane M. O. Vale UFRN
  • Anne M. P. Canuto anne@dimap.ufrn.br
DOI: https://doi.org/10.5753/eniac.2018.4466

Resumo


Semi-supervised learning algorithms are able to train classifiers from a small portion of initially labeled objects. The reliability of the classification process depends on several factors that include the type of classifier used and a set of parameters that customize them. One of the most important factors is a threshold that determines which instances are included per iteration, allowing to label only instances with high confidence values. This article analyzes different values for the variation factor of the FlexCon-C algorithm and measures the impact of this change on its accuracy. The results consider thirty different databases, four classifiers and five different percentages of pre-labeled data.

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Publicado
22/10/2018
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GORGÔNIO, Arthur C.; ALVES, Cainan T.; LUCENA, Amarildo J. F.; GORGÔNIO, Flavius L.; VALE, Karliane M. O.; CANUTO, Anne M. P.. Analysis of the Threshold Variation of the FlexCon-C Algorithm for Semi-supervised Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 775-786. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2018.4466.