Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem

  • Yann Chevaleyre
  • Jean-Daniel Zucker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2056)

Abstract

In recent work, Dietterich et al. (1997) have presented the problem of supervised multiple-instance learning and how to solve it by building axis-parallel rectangles. This problem is encountered in contexts where an object may have different possible alternative configurations, each of which is described by a vector. This paper introduces the multiple-part problem, which is related to the multiple-instance problem, and shows how it can be solved using the multiple-instance algorithms. These two so-called “multiple“ problems could play a key role both in the development of efficient algorithms for learning the relations between the activity of a structured object and its structural properties and in relational learning. This paper analyzes and tries to clarify multiple-problem solving. It goes on to propose multiple-instance extensions of classical learning algorithms to solve multiple-problems by learning multiple-decision trees (Id3-Mi) and multiple-decision rules (Ripper- Mi). In particular, it suggests a new multiple-instance entropy function and a multiple-instance coverage function. Finally, it successfully applies the multiple-part framework on the well-known mutagenesis prediction problem.

Keywords

Inductive Logic Programming Linearity Hypothesis Multiple Instance Learning Learn Decision Tree Inductive Logic Programming System 
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 2001

Authors and Affiliations

  • Yann Chevaleyre
    • 1
  • Jean-Daniel Zucker
    • 1
  1. 1.LIP6-CNRSUniversity Paris VIParisFrance

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