Classification techniques with minimal labelling effort and application to medical reports

Fathi H. Saad, G. Duncan Bell, Beatriz de la Iglesia

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)268-287
Number of pages20
JournalInternational Journal of Data Mining and Bioinformatics
Issue number3
Publication statusPublished - Sep 2008

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