BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Bayesian network model for semi-structured document classification
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Question Classification Based on Incremental Modified Bayes
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 02
A decision-tree-based symbolic rule induction system for text categorization
IBM Systems Journal
Order Independent Incremental Evolving Fuzzy Grammar Fragment Learner
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
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Text mining saves the necessity to sift through vast amount of documents manually to find relevant information. This paper focuses on text categorization, one of the tasks under text mining. This paper introduces fuzzy grammar as a technique for building text classifier and investigates the performance of fuzzy grammar against other machine learning methods such as decision table, support vector machine, statistic, nearest neighbor and boosting. Incidents dataset was used where the focus was given on classifying the incidents events. Results have shown that fuzzy grammar has gotten promising results among the other benchmark machine learning methods.