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A Framework for Learning Rules from Multiple Instance Data

  • Yann Chevaleyre
  • Jean-Daniel Zucker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2167)

Abstract

This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. In a multiple-instance representation, each learning example is represented by a bag” of fixed-length feature vectors”.Such a representation,lying somewhere between propositional and first-order representation, offers a tradeoff between the two. Naive-RipperMi is one implementation of this extension on the rule learning algorithm Ripper. Several pitfalls encountered by this naive extension during induction are explained. A new multiple-instance search bias based on decision tree techniques is then used to avoid these pitfalls. Experimental results show the benefits of this approach for solving propositionalized relational problems in terms of speed and accuracy.

Keywords

Multiple Instance Target Concept Inductive Logic Programming Candidate Rule Decision Tree Technique 
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 2003

Authors and Affiliations

  • Yann Chevaleyre
    • 1
  • Jean-Daniel Zucker
    • 1
  1. 1.LIP6-CNRSUniversity Paris VIParis Cedex 05France

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