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 [e-mail]

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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