Induction from multi-label training examples in text categorization: Combining subclassifiers (a case study)

Kanoksri Sarinnapakorn, Miroslav Kubat

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

Abstract

The main problem with algorithms for induction from multi-label training examples is that they often suffer from prohibitive computational costs, especially in text-categorization domains with thousands of features to characterize each document. One way to reduce these costs is to run a baseline induction algorithm separately for different subsets of features, obtaining a set of subclassifiers to be then combined. In this case study, we investigate three different ways to combine subclassifiers, including our own solution based on the Dempster-Shafer Theory.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007
Pages351-357
Number of pages7
StatePublished - Dec 1 2007
Event2007 International Conference on Artificial Intelligence, ICAI 2007 - Las Vegas, NV, United States
Duration: Jun 25 2007Jun 28 2007

Publication series

NameProceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007
Volume1

Other

Other2007 International Conference on Artificial Intelligence, ICAI 2007
CountryUnited States
CityLas Vegas, NV
Period6/25/076/28/07

Keywords

  • Dempster-Shafer Theory
  • Multi-label examples
  • Text categorization

ASJC Scopus subject areas

  • Artificial Intelligence

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