Introduction to non-linear optimization
Introduction to non-linear optimization
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)
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
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
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A common way of performing Feature Selection in Text Categorization consists in keeping the features with highest score according to certain measures, like linear ones which have been successfully proposed in [1]. Its disadvantage is that they need to previously determine the parameter which defines them. Until now, this drawback has been overcome by taking manually a set of values for such parameter. This paper proposes a method for automatically determining optimal values of the parameter by means of solving a univariate maximization problem.