Detecting and revising misclassifications using ILP

Authors:
Masaki Yokoyama;Tohgoroh Matsui;Hayato Ohwada
Affiliations:
Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan;Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan;Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan
Venue:
DS'05 Proceedings of the 8th international conference on Discovery Science
Year:
2005

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Abstract

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.