The probability ranking principle in IR
Readings in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
On feature distributional clustering for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A meta-learning approach for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Asian Language Information Processing (TALIP)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Using asymmetric distributions to improve text classifier probability estimates
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Classification of textual E-mail spam using data mining techniques
Applied Computational Intelligence and Soft Computing
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It is common that representative words in a document are identified and discriminated by their statistical distribution of their frequency statistics. We assume that evaluating the confidence measure of terms through content-based document analysis leads to a better performance than the parametric assumptions of the standard frequency-based method. In this paper, we propose a new approach of term weighting method that replaces the frequency-based probabilistic methods. Experiments on Naïve Bayesian classifiers showed that our approach achieved an improvement compared to the frequency-based method on each point of the evaluation.