Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07)
Text Categorization for Multi-label Documents and Many Categories
Maribor, Slovenia
June 20-June 22
ISBN: 0-7695-2905-4
DOI Bookmark:
http://doi.ieeecomputersociety.org/10.1109/CBMS.2007.108
In this paper, we propose a new classification method that addresses classification in multiple categories of textual documents. We call it Matrix Regression (MR) due to its resemblance to regression in a high dimensional space. Experiences on a medical corpus of hospital records to be classified by ICD (International Classification of Diseases) code demonstrate the validity of the MR approach. We compared MR with three frequently used algorithms in text categorization that are k-Nearest Neighbors, Centroide and Support Vector Machine. The experimental results show that our method outperforms them in both precision and time of classification.
Citation:
I. Sandu Popa, K. Zeitouni, G. Gardarin, D. Nakache, E. Metais, "Text Categorization for Multi-label Documents and Many Categories," cbms, pp.421-426, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07), 2007
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