Classification techniques with minimal labelling effort and application to medical reports

Saad, Fathi H., Bell, G. Duncan and de la Iglesia, Beatriz (2008) Classification techniques with minimal labelling effort and application to medical reports. International Journal of Data Mining and Bioinformatics, 2 (3). pp. 268-287. ISSN 1748-5673

Full text not available from this repository. (Request a copy)

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.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	Vishal Gautam
Date Deposited:	10 Mar 2011 10:54
Last Modified:	06 Feb 2025 02:37
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22377
DOI:	10.1504/IJDMB.2008.022638

Actions (login required)

View Item