Comparison of Documents Classification Techniques to Classify Medical Reports

F. Saad, B. de la Iglesia, G. D. Bell

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)


This paper addresses a real world problem: the classification of text documents in the medical domain. There are a number of approaches to classifying text documents. Here, we use a partially supervised classification approach and argue that it is effective and computationally efficient for real-world problems. The approach uses a two-step strategy to cut down on the effort required to label each document for classification. Only a small set of positive documents are labeled initially, with others being labeled automatically as a result of the first step. The second step builds the actual text classifier. There are a number of methods that have been proposed for each step. A comprehensive evaluation of various combinations of methods is conducted to compare their performances using real world medical documents. The results show that using EM based methods 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 the performance of the classifiers.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
EditorsWee-Keong Ng, Masaru Kitsuregawa, Jianzhong Li, Kuiyu Chang
PublisherSpringer Berlin / Heidelberg
Number of pages7
ISBN (Print)978-3-540-33206-0
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg

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