A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
e-mail authentication system: a spam filtering for smart senders
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Batch-Mode Active Learning with Semi-supervised Cluster Tree for Text Classification
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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This paper proposes an interactive spam filtering method that utilizes active learning and feature selection. Identifying effective features are very important in spam filtering because spam mails include so many meaningless words that are slightly different from each other. Thus identifying effective and ineffective features is promising approach.Although traditional feature selection methods have been done based on some amount of labeled training data, this assumption does not hold in interactive spam filtering. We propose a method to identify effective features through active learning in spam filtering using naive Bayes approach. Experimental results show that our method outperforms traditional methods that operate with no feature selection.