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Confidence Measure for Czech Document Classification

  • Pavel Král
  • Ladislav Lenc
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9042)

Abstract

This paper deals with automatic document classification in the context of a real application for the Czech News Agency (ČTK). The accuracy our classifier is high, however it is still important to improve the classification results. The main goal of this paper is thus to propose novel confidence measure approaches in order to detect and remove incorrectly classified samples. Two proposed methods are based on the posterior class probability and the third one is a supervised approach which uses another classifier to determine if the result is correct. The methods are evaluated on a Czech newspaper corpus. We experimentally show that it is beneficial to integrate the novel approaches into the document classification task because they significantly improve the classification accuracy.

Keywords

Latent Dirichlet Allocation Acceptance Threshold Supervise Approach Conformal Predictor Automatic Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pavel Král
    • 1
    • 2
  • Ladislav Lenc
    • 1
    • 2
  1. 1.Dept. of Computer Science & Engineering, Faculty of Applied SciencesUniversity of West BohemiaPlzeňCzech Republic
  2. 2.NTIS - New Technologies for the Information Society, Faculty of Applied SciencesUniversity of West BohemiaPlzeňCzech Republic

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