Evaluation of Stacking on Biomedical Data

Autores

  • Maria Izabela Ruz Caffé Universidade de São Paulo
  • Pedro Santoro Perez Universidade de São Paulo
  • José Augusto Baranauskas Universidade de São Paulo

Palavras-chave:

Artificial Intelligence, Classification, Ensembles

Resumo

Objectives: Stacking is a well-known  ensemble technique, but some of its aspects still need to be explored, e.g., there are few recommendations on which and how many algorithms should be used at level-0 or even which algorithm should be used to compose the level-1 meta-classifier. The literature indicates the meta-algorithm at level-1 should be simple, and Naive Bayes has been typically used in these studies. Methods: In this work, we have analyzed stacking on biomedical datasets, using three different paradigms of machine learning algorithms to compose the meta-classifier. Results: The experiments indicate simple meta-algorithms do not provide good results. Conclusion: the meta-classifier must have a degree of complexity to provide a nice performance.

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Publicado

25-09-2012

Como Citar

Ruz Caffé, M. I., Santoro Perez, P., & Baranauskas, J. A. (2012). Evaluation of Stacking on Biomedical Data. Journal of Health Informatics, 4(3). Recuperado de https://jhi.sbis.org.br/index.php/jhi-sbis/article/view/181

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