Hybrid feature selection methods for online biomedical publication classification

Long Ma, Yanqing Zhang, Raj Sunderraman, Peter T. Fox, Angela R. Laird, Jessica A. Turner, Matthew D. Turner

Producción científica: Conference contribution

3 Citas (Scopus)

Resumen

We review several feature selection methods: Recursive Feature Elimination, Select K Best, and Random Forests, as elements of a processing chain for feature selection in a text mining task. The text mining task is a multi-label classification problem of label assignment; metadata that is usually applied to published scientific papers by expert curators. In the formulation of this classification task, a feature space that is dramatically larger than the available training data occurs naturally and inevitably. We explore ways to reduce the dimension of the feature space, and show that sequential feature selection does substantially improve performance for this complex type of data.

Idioma originalEnglish (US)
Título de la publicación alojada2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781479969265
DOI
EstadoPublished - oct 16 2015
EventoIEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015 - Niagara Falls, Canada
Duración: ago 12 2015ago 15 2015

Serie de la publicación

Nombre2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015

Other

OtherIEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
País/TerritorioCanada
CiudadNiagara Falls
Período8/12/158/15/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Health Informatics
  • Artificial Intelligence
  • Biomedical Engineering

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