Classification techniques with minimal labelling effort and application to medical reports
by Fathi H. Saad, G. Duncan Bell, Beatriz De la Iglesia
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 2, No. 3, 2008

Abstract: There are a number of approaches to classify text documents. Here, we use Partially Supervised Classification (PSC) and argue that it is an effective and efficient approach for real-world problems. PSC uses a two-step strategy to cut down on the labelling effort. There are a number of methods that have been proposed for each step. An evaluation of various methods is conducted using real-world medical documents. The results show that using EM to build the classifier yields better results than SVM. We also experimentally show that careful selection of a subset of features to represent the documents can improve performance.

Online publication date: Thu, 22-Jan-2009

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