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.
|Number of pages||20|
|Journal||International Journal of Data Mining and Bioinformatics|
|Publication status||Published - Sep 2008|