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Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction

  • Jun Zhang
  • Adrian Silvescu
  • Vasant Honavar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2371)

Abstract

Most learning algorithms for data-driven induction of pattern classifiers (e.g., the decision tree algorithm), typically represent input patterns at a single level of abstraction – usually in the form of an ordered tuple of attribute values. However, in many applications of inductive learning – e.g., scientific discovery, users often need to explore a data set at multiple levels of abstraction, and from different points of view. Each point of view corresponds to a set of ontological (and representational) commitments regarding the domain of interest. The choice of an ontology induces a set of representatios of the data and a set of transformations of the hypothesis space. This paper formalizes the problem of inductive learning using ontologies and data; describes an ontology-driven decision tree learning algorithm to learn classification rules at multiple levels of abstraction; and presents preliminary results to demonstrate the feasibility of the proposed approach.

Keywords

Decision Tree Information Gain Candidate Attribute Decision Tree Algorithm Hypothesis Space 
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 2002

Authors and Affiliations

  • Jun Zhang
    • 1
  • Adrian Silvescu
    • 1
  • Vasant Honavar
    • 1
  1. 1.Artificial Intelligence Research Laboratory Department of Computer ScienceIowa State UniversityAmesUSA

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