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A Comparative Study on Term Weighting Schemes for Text Classification

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Machine Learning, Optimization, and Big Data (MOD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

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Abstract

Text Classification (or Text Categorization) is a popular machine learning task. It consists in assigning categories to documents. In this paper, we are interested in comparing state of the art classifiers and state of the art feature weights. Feature weight methods are classic tools that are used in text categorization. We extend previous studies by evaluating numerous term weighting schemes for state of the art classification methods. We aim at providing a complete survey on text classification for fair benchmark comparisons.

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Notes

  1. 1.

    http://disi.unitn.it/moschitti/corpora.htm.

  2. 2.

    http://disi.unitn.it/moschitti/corpora.htm.

  3. 3.

    http://qwone.com/~jason/20Newsgroups/.

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Author information

Authors and Affiliations

  1. LISIC, Université du Littoral Côte d’Opale, 50 Rue Ferdinand Buisson, 62100, Calais, France

    Ahmad Mazyad, Fabien Teytaud & Cyril Fonlupt

Authors
  1. Ahmad Mazyad
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  2. Fabien Teytaud
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  3. Cyril Fonlupt
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Corresponding author

Correspondence to Ahmad Mazyad .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Mazyad, A., Teytaud, F., Fonlupt, C. (2018). A Comparative Study on Term Weighting Schemes for Text Classification. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

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