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International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

CIBB 2015: Computational Intelligence Methods for Bioinformatics and Biostatistics pp 98–113Cite as

Supervised Term Weights for Biomedical Text Classification: Improvements in Nearest Centroid Computation

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9874))

Abstract

Maintaining accessibility of biomedical literature databases has led to development of text classification systems to assist human indexers by recommending thematic categories to biomedical articles. These systems rely on using machine learning methods to learn the association between the document terms and predefined categories. The accuracy of a text classification method depends on the metric used in order to assign a weight to each term. Weighting metrics can be classified as supervised or unsupervised according to whether they use prior information on the number of documents belonging to each category. In this paper, we propose two supervised weighting metrics (One-way Klosgen and Loevinger) which both improve the quality of biomedical document classification. We also show that by using moment generating function centroids, an alternative to the traditional arithmetical average centroids, a nearest centroid classifier with Loevinger metric performs significantly better than SVM on a biomedical text classification task.

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Notes

  1. 1.

    Table 1 contains the abbreviations used in this paper.

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Authors and Affiliations

  1. Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes (LITIS), Université de Rouen, 76821, Mont-Saint-Aignan Cedex, France

    Mounia Haddoud, Thierry Lecroq & Saïd Abdeddaïm

  2. Recherche en Informatique Intelligente, Mathématiques et Applications (RIIMA), USTHB, BP 32, El-Alia, Bab-ezzouar, 16111, Algiers, Algeria

    Mounia Haddoud & Aïcha Mokhtari

Authors
  1. Mounia Haddoud
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  2. Aïcha Mokhtari
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  3. Thierry Lecroq
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  4. Saïd Abdeddaïm
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Corresponding author

Correspondence to Saïd Abdeddaïm .

Editor information

Editors and Affiliations

  1. CNR, Istituto per le Applicazioni del Calcolo, Naples, Italy

    Claudia Angelini

  2. Center for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milano, Italy

    Paola MV Rancoita

  3. DIBRIS, University of Genoa, Genova, Italy

    Stefano Rovetta

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Haddoud, M., Mokhtari, A., Lecroq, T., Abdeddaïm, S. (2016). Supervised Term Weights for Biomedical Text Classification: Improvements in Nearest Centroid Computation. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-44332-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44331-7

  • Online ISBN: 978-3-319-44332-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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