A philosophical basis for knowledge acquisition
Knowledge Acquisition
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
EMMA: An E-Mail Management Assistant
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Generating summary keywords for emails using topics
Proceedings of the 13th international conference on Intelligent user interfaces
A Large-Scale Evaluation of an E-mail Management Assistant
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Exploiting concept clumping for efficient incremental e-mail categorization
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Exploiting concept clumping for efficient incremental news article categorization
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Knowledge acquisition for categorization of legal case reports
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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In this paper, we present and use a method for e-mail categorization based on simple term statistics updated incrementally. We apply simple term statistics to two different tasks. The first task is to predict folders for classification of e-mails when large numbers of messages are required to remain unclassified. The second task is to support users who define rule bases for the same classification task, by suggesting suitable keywords for constructing Ripple Down Rule bases in this scenario. For both tasks, the results are compared with a number of standard machine learning algorithms. The comparison shows that the simple term statistics method achieves a higher level of accuracy than other machine learning methods when taking computation time into account.