The nature of statistical learning theory
The nature of statistical learning theory
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
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
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
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
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We propose a feature called category browsing to enhance the full-text search function of Thai-language news article search engine. The category browsing allows users to browse and filter search results based on some predefined categories. To implement the category browsing feature, we applied and compared among several text categorization algorithms including decision tree, Naive Bayes (NB) and Support Vector Machines (SVM). To further increase the performance of text categorization, we performed evaluation among many feature selection techniques including document frequency thresholding (DF), information gain (IG) and Chi-Squared (CHI). Based on our experiments using a large news corpus, the SVM algorithm with the IG feature selection yielded the best performance with the F1 measure equal to 95.42%.