Combining active learning and graph-based semi-supervised learning

  • Jhonatan Candao Universidade Federal de São Paulo
  • Lilian Berton Universidade de São Paulo

Resumo


The scarcity of labeled data is a common problem in many applications. Semi-supervised learning (SSL) aims to minimize the need for human annotation combining a small set of label data with a huge amount of unlabeled data. Similarly to SSL, Active Learning (AL) reduces the annotation efforts selecting the most informative points for annotation. Few works explore AL and graph-based SSL, in this work, we combine both strategies and explore different techniques: two graph-based SSL and two query strategy of AL in a pool-based scenario. Experimental results in artificial and real datasets indicate that our approach requires significantly less labeled instances to reach the same performance of random label selection.

Palavras-chave: Machine learning, Semi-supervised learning, Active learning, Label propagation

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Publicado
15/10/2019
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CANDAO, Jhonatan; BERTON, Lilian. Combining active learning and graph-based semi-supervised learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 694-704. DOI: https://doi.org/10.5753/eniac.2019.9326.