| Classification techniques with minimal labelling effort and application to medical reports. | |
| | |
MedLine Citation:
|
PMID: 19024498 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
|
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. |
| | |
Authors:
|
Fathi H Saad; G Duncan Bell; Beatriz de la Iglesia |
Related Documents
:
|
17884328 - The fear of positive evaluation scale: assessing a proposed cognitive component of soci... 9231128 - Continuing medical education: the question of evaluation. 19160628 - A 31-year-old army specialist presenting with acute oligoarthritis. 6806748 - A versatile argon microsurgical laser. 9109328 - Informatics in the care of patients: ten notable challenges. 18430278 - Telemedicine via satellite to support offshore oil platforms. |
Publication Detail:
|
Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
|
Title: International journal of data mining and bioinformatics Volume: 2 ISSN: 1748-5673 ISO Abbreviation: - Publication Date: 2008 |
Date Detail:
|
Created Date: 2008-11-21 Completed Date: 2008-12-30 Revised Date: - |
Medline Journal Info:
|
Nlm Unique ID: 101279469 Medline TA: Int J Data Min Bioinform Country: Switzerland |
Other Details:
|
Languages: eng Pagination: 268-87 Citation Subset: IM |
Affiliation:
|
School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK. fathi.saad@uea.ac.uk |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
Artificial Intelligence* Database Management Systems* Documentation / methods* Great Britain Information Storage and Retrieval / methods* Medical Records Systems, Computerized* Natural Language Processing* Pattern Recognition, Automated / methods* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: A Bayesian framework for knowledge driven regression model in micro-array data analysis.
Next Document: Evaluation of culture media for Paenibacillus larvae applied to studies of antimicrobial activity.