Meta learning on small biomedical datasets

İBRİKÇİ T., Karabulut E. M., Uwisengeyimana J. D.

International Conference on Information Science and Applications, ICISA 2016, Minh City, Vietnam, 15 - 18 February 2016, vol.376, pp.933-939, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 376
  • Doi Number: 10.1007/978-981-10-0557-2_89
  • City: Minh City
  • Country: Vietnam
  • Page Numbers: pp.933-939
  • Çukurova University Affiliated: Yes

Abstract

Meta-learning is one of subsections of supervised machine learning that
has continuously grown with interests to apply on new data sets in the late years.
Meta learning is the process of knowledge that is acquired by the examples. Bagging,
dagging, decorate, rotation forest, and filtered classifiers are well known
meta-learning algorithms that are performed to compare with these meta-learning
algorithms on 8 different biomedical datasets. In these algorithms, the rotation
forest had the better results according to F-measurement and ROC Area in most
cases.