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The Class Imbalance Problem in Construction of Training Datasets for Authorship Attribution

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Man–Machine Interactions 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 391))

Abstract

The paper presents research on class imbalance in the context of construction of training sets for authorship recognition. In experiments the sets are artificially imbalanced, then balanced by under-sampling and over-sampling. The prepared sets are used in learning of two predictors: connectionist and rule-based, and their performance observed. The tests show that for artificial neural networks in several cases the predictive accuracy is not degraded but in fact improved, while one rule classifier is highly sensitive to class balance as it never performs better than for the original balanced set and in many cases worse.

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Acknowledgments

The research described was performed within the project BK/RAu2/2015 at the Institute of Informatics, Silesian University of Technology, Gliwice, Poland.

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

Authors
  1. Urszula Stańczyk
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Corresponding author

Correspondence to Urszula Stańczyk .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Brachman

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

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Stańczyk, U. (2016). The Class Imbalance Problem in Construction of Training Datasets for Authorship Attribution. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_46

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  • DOI: https://doi.org/10.1007/978-3-319-23437-3_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23436-6

  • Online ISBN: 978-3-319-23437-3

  • eBook Packages: EngineeringEngineering (R0)

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