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Automatic Text Categrization Using Hybrid Multiple Model Schemes

  • Journal of the Korean Society for Information Management
  • Abbr : JKOSIM
  • 2002, 19(4), pp.35~51
  • DOI : 10.3743/KOSIM.2002.19.4.035
  • Publisher : 한국정보관리학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : October 30, 2002
  • Accepted : November 30, 2002
  • Published : December 30, 2002

Myoung, Soon-Hee 1 KIM IN CHEOL ORD ID 2

1용인송담대학
2경기대학교

Accredited

ABSTRACT

Inductive learning and classification techniques have been employed in various research and applications that organize textual data to solve the problem of information access. In this study, we develop hybrid model combination methods which incorporate the concepts and techniques for multiple modeling algorithms to improve the accuracy of text classification, and conduct experiments to evaluate the performances of proposed schemes. Boosted stacking, one of the extended stacking schemes proposed in this study yields higher accuracy relative to the conventional model combination methods and single classifiers.

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