Learning classifiers using hierarchically structured class taxonomies

Feihong Wu, Jun Zhang, Vasant Honavar

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

31 Scopus citations

Abstract

We consider classification problems in which the class labels are organized into an abstraction hierarchy in the form of a class taxonomy. We define a structured label classification problem. We explore two approaches for learning classifiers in such a setting. We also develop a class of performance measures for evaluating the resulting classifiers. We present preliminary results that demonstrate the promise of the proposed approaches.

Original languageEnglish (US)
Title of host publicationAbstraction, Reformulation and Approximation - 6th International Symposium, SARA 2005, Proceedings
PublisherSpringer Verlag
Pages313-320
Number of pages8
ISBN (Print)3540278729, 9783540278726
DOIs
StatePublished - 2005
Event6th International Symposium on Abstraction, Reformulation and Approximation, SARA 2005 - Airth Castle, Scotland, United Kingdom
Duration: Jul 26 2005Jul 29 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3607 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Symposium on Abstraction, Reformulation and Approximation, SARA 2005
CountryUnited Kingdom
CityAirth Castle, Scotland
Period7/26/057/29/05

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Wu, F., Zhang, J., & Honavar, V. (2005). Learning classifiers using hierarchically structured class taxonomies. In Abstraction, Reformulation and Approximation - 6th International Symposium, SARA 2005, Proceedings (pp. 313-320). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3607 LNAI). Springer Verlag. https://doi.org/10.1007/11527862_24