A Robust Pseudo-label Reevaluation Strategy for the Self-training Algorithm

• Luiz M. S. Silva UFRN
• Renan M. R. A. Costa UFRN
• José A. A. Paiva UFRN
• Arthur C. Gorgônio UFRN
• Karliane M. O. Vale UFRN
• Flavius L. Gorgônio UFRN

DOI: https://doi.org/10.5753/eniac.2025.13905

Resumo

Este trabalho propõe uma extensão do algoritmo Self-Training com reavaliação iterativa de pseudo-rótulos, utilizando a métrica de silhueta para identificar e remover instâncias ruidosas, e um comitê de classificadores com votação ponderada para reforçar decisões em casos de baixa confiança. A abordagem visa mitigar a propagação de erros e aumentar a robustez do aprendizado semissupervisionado. Avaliações realizadas em 18 bases de dados demonstraram desempenho superior ao self-training original em termos de acurácia, F1-score e estabilidade, especialmente em cenários com poucos dados rotulados.

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