Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
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
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Journal of the American Society for Information Science and Technology
Introducing a Family of Linear Measures for Feature Selection in Text Categorization
IEEE Transactions on Knowledge and Data Engineering
A probabilistic model for compact document topic representation
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
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Text Categorization, which consists of automatically assigning documents to a set of categories, usually involves the management of a huge number of features. Most of them are irrelevant or introduce noise which misleads the classifiers. Thus, feature reduction is often performed in order to increase the efficiency and effectiveness of the classification. In this paper we propose to select relevant features by means of what we call Angular Measures, which are simpler than other usual measures applied for this purpose. We carry out experiments over two different corpora and find that the proposed measures perform equal or better than some of the existing ones.