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Supervised Classification Using Balanced Training

  • Mian Du
  • Matthew Pierce
  • Lidia Pivovarova
  • Roman Yangarber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8791)

Abstract

We examine supervised learning for multi-class, multi-label text classification. We are interested in exploring classification in a real-world setting, where the distribution of labels may change dynamically over time. First, we compare the performance of an array of binary classifiers trained on the label distribution found in the original corpus against classifiers trained on balanced data, where we try to make the label distribution as nearly uniform as possible. We discuss the performance trade-offs between balanced vs. unbalanced training, and highlight the advantages of balancing the training set. Second, we compare the performance of two classifiers, Naive Bayes and SVM, with several feature-selection methods, using balanced training. We combine a Named-Entity-based rote classifier with the statistical classifiers to obtain better performance than either method alone.

Keywords

Text categorisation Information extraction 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mian Du
    • 1
  • Matthew Pierce
    • 1
  • Lidia Pivovarova
    • 1
  • Roman Yangarber
    • 1
  1. 1.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland

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