Ontology-driven induction of decision trees at multiple levels of abstraction

Jun Zhang, Adrian Silvescu, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAbstraction, Reformulation, and Approximation - 5th International Symposium, SARA 2002, Proceedings
EditorsSven Koenig, Robert C. Holte
PublisherSpringer Verlag
Pages316-323
Number of pages8
ISBN (Print)3540439412, 9783540439417
DOIs
StatePublished - 2002
Event5th International Symposium on Abstraction, Reformulation, and Approximation, SARA 2002 - Kananaskis, Canada
Duration: Aug 2 2002Aug 4 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2371
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Symposium on Abstraction, Reformulation, and Approximation, SARA 2002
CountryCanada
CityKananaskis
Period8/2/028/4/02

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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