Fast induction of multiple decision trees in text categorization from large scale, imbalanced, and multi-label data

Peerapon Vateekul, Miroslav Kubat

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

13 Scopus citations

Abstract

The paper focuses on automated categorization of text documents, each labeled with one or more classes and described by tens of thousands of features. The computational costs of induction in such domains are so high as almost to disqualify the use of decision trees; the reduction of these costs is thus an important research issue. Our own solution, FDT ("fast decision-tree induction"), uses a two-pronged strategy: (1) feature-set pre-selection, and (2) induction of several trees, each from a different data subset, with the combination of the results from multiple trees with a data-fusion technique tailored to domains with imbalanced classes.

Original languageEnglish (US)
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages320-325
Number of pages6
DOIs
StatePublished - Dec 1 2009
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 6 2009

Publication series

NameICDM Workshops 2009 - IEEE International Conference on Data Mining

Other

Other2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
CountryUnited States
CityMiami, FL
Period12/6/0912/6/09

Keywords

  • Decision tree
  • Imbalanced classes
  • Large-scale data
  • Multi-label examples
  • Text categorization

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Vateekul, P., & Kubat, M. (2009). Fast induction of multiple decision trees in text categorization from large scale, imbalanced, and multi-label data. In ICDM Workshops 2009 - IEEE International Conference on Data Mining (pp. 320-325). [5360425] (ICDM Workshops 2009 - IEEE International Conference on Data Mining). https://doi.org/10.1109/ICDMW.2009.94