Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
The nature of statistical learning theory
The nature of statistical learning theory
An algorithm for suffix stripping
Readings in information retrieval
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining clustering and co-training to enhance text classification using unlabelled data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Beyond TFIDF weighting for text categorization in the vector space model
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning from positive and unlabeled examples with different data distributions
ECML'05 Proceedings of the 16th European conference on Machine Learning
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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.