Detecting and Revising Misclassifications Using ILP

  • Masaki Yokoyama
  • Tohgoroh Matsui
  • Hayato Ohwada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)


This paper proposes a method for detecting misclassifications of a classification rule and then revising them. Given a rule and a set of examples, the method divides misclassifications by the rule into miscovered examples and uncovered examples, and then, separately, learns to detect them using Inductive Logic Programming (ILP). The method then combines the acquired rules with the initial rule and revises the labels of misclassified examples. The paper shows the effectiveness of the proposed method by theoretical analysis. In addition, it presents experimental results, using the Brill tagger for Part-Of-Speech (POS) tagging.


Logic Program Target Concept Weak Learner Inductive Logic Programming Initial Rule 
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-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Masaki Yokoyama
    • 1
  • Tohgoroh Matsui
    • 1
  • Hayato Ohwada
    • 1
  1. 1.Department of Industrial Administration, Faculty of Science and TechnologyTokyo University of ScienceChibaJapan

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