Learning ontology-Aware classifiers

Jun Zhang, Doina Caragea, Vasant Honavar

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

8 Scopus citations

Abstract

Many practical applications of machine learning in data-driven scientific discovery commonly call for the exploration of data from multiple points of view that correspond to explicitly specified ontologies. This paper formalizes a class of problems of learning from ontology and data, and explores the design space of learning classifiers from attribute value taxonomies (AVTs) and data. We introduce the notion of AVT-extended data sources and partially specified data. We propose a general framework for learning classifiers from such data sources. Two instantiations of this framework, AVT-based Decision Tree classifier and AVT-based Naïve Bayes classifier are presented. Experimental results show that the resulting algorithms are able to learn robust high accuracy classifiers with substantially more compact representations than those obtained by standard learners.

Original languageEnglish (US)
Title of host publicationDiscovery Science - 8th International Conference, DS 2005, Proceedings
PublisherSpringer Verlag
Pages308-321
Number of pages14
ISBN (Print)3540292306, 9783540292302
DOIs
StatePublished - 2005
Event8th International Conference on Discovery Science, DS 2005 - , Singapore
Duration: Oct 8 2005Oct 11 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3735 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Discovery Science, DS 2005
CountrySingapore
Period10/8/0510/11/05

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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