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Comparative Study of Feature Selection Methods for Medical Full Text Classification

  • Carlos Adriano Gonçalves
  • Eva Lorenzo Iglesias
  • Lourdes Borrajo
  • Rui CamachoEmail author
  • Adrián Seara Vieira
  • Célia Talma Gonçalves
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11466)

Abstract

There is a lot of work in text categorization using only the title and abstract of the papers. However, in a full paper there is a much larger amount of information that could be used to improve the text classification performance. The potential benefits of using full texts come with an additional problem: the increased size of the data sets.

To overcome the increased the size of full text data sets we performed an assessment study on the use of feature selection methods for full text classification. We have compared two existing feature selection methods (Information Gain and Correlation) and a novel method called k-Best-Discriminative-Terms. The assessment was conducted using the Ohsumed corpora. We have made two sets of experiments: using title and abstract only; and full text.

The results achieved by the novel method show that the novel method does not perform well in small amounts of text like title and abstract but performs much better for the full text data sets and requires a much smaller number of attributes.

Keywords

Text classification Feature selection Medical texts corpus 

Notes

Acknowledgements

This work was supported by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. This work was also partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Carlos Adriano Gonçalves
    • 1
    • 3
  • Eva Lorenzo Iglesias
    • 1
  • Lourdes Borrajo
    • 1
  • Rui Camacho
    • 2
    • 3
    Email author
  • Adrián Seara Vieira
    • 1
  • Célia Talma Gonçalves
    • 4
    • 5
  1. 1.Computer Science DepartmentUniversity of Vigo, Escola Superior de Enxeñería InformáticaOurenseSpain
  2. 2.Faculdade de Engenharia da Universidade do PortoPortoPortugal
  3. 3.LIAAD - INESC TECPortoPortugal
  4. 4.CEOS.PP/ISCAP-P.PORTOPortoPortugal
  5. 5.LIACCPortoPortugal

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