Text Categorization for Multi-label Documents and Many Categories

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.

• Type: Conference
• Year: 2007
• Where: CBMS
• Authors: Iulian Sandu Popa, Karine Zeitouni, Georges Gardarin, Didier Nakache, Elisabeth Métais
• Keywords: CBMS 2007, International Classification, Medical Imaging, MR Approach, Support Vector Machine