Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
The nature of statistical learning theory
The nature of statistical learning theory
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
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Journal of the American Society for Information Science and Technology
Scoring and Selecting Terms for Text Categorization
IEEE Intelligent Systems
Introducing a Family of Linear Measures for Feature Selection in Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Set Cover Feature Selection for Text Categorisation and spam detection
International Journal of Advanced Intelligence Paradigms
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Text Categorisation (TC) consists of automatically assigning documents to a set of prefixed categories. It usually involves the management of a huge number of features. Some of them are irrelevant or noisy which mislead the classifiers. Thus, they are reduced to increase the efficiency and effectiveness of the classification. In this paper we propose to select relevant features using two different families of filtering measures, which are simpler than other usual measures applied for this purpose. The experiments over three corpora show that, in general, the proposed measures perform equal or better than the existing ones, sometimes allowing greater reductions.