Naı̈ve-Bayes vs. Rule-Learning in Classification of Email
نویسنده
چکیده
Recent growth in the use of email for communication and the corresponding growth in the volume of email received has made automatic processing of email desirable. Two learning methods, naı̈ve bayesian learning with bag-valued features and the RIPPER rule-learning algorithm have shown promise in other text categorization tasks. I present three experiments in automatic mail foldering and spam filtering, showing that naı̈ve bayes outperforms RIPPER in classification accuracy.
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