Combining Classifiers in Text Categorization.

Leah S. Larkey, W. Bruce Croft: Combining Classifiers in Text Categorization. SIGIR 1996: 289-297

@inproceedings{DBLP:conf/sigir/LarkeyC96,
  author    = {Leah S. Larkey and
               W. Bruce Croft},
  title     = {Combining Classifiers in Text Categorization},
  booktitle = {SIGIR},
  year      = {1996},
  pages     = {289-297},
  ee        = {db/conf/sigir/LarkeyC96.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

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Abstract

Three different types of classifiers were investigated in the context of a text categorization problem in the medical domain: the automatic assignment of ICD9 codes to dictated inpatient discharge summaries. K-nearest-neighbor, relevance feedback, and Bayesian independence classifiers were applied individually and in combination. A combination of different classifiers produced better results than any single type of classifier. For this specific medical categorization problem, new query formulation and weighting methods used in the k-nearest-neighbor classifier improved performance.

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Hans-Peter Frei, Donna Harman, Peter Schäuble, Ross Wilkinson (Eds.): Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'96, August 18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum). ACM 1996, ISBN 0-89791-792-8
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