Incremental E-Mail Classification and Rule Suggestion Using Simple Term Statistics
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
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
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EMMA is an e-mail management assistant based on Ripple Down Rules, providing a high degree of classification accuracy while simplifying the task of maintaining the consistency of the rule base. A Naive Bayes algorithm is used to improve the usability of EMMA by suggesting keywords to help the user define rules. In this paper, we report on an experimental evaluation of EMMA on 16 998 pre-classified messages. The aim of the evaluation was to show that the Ripple Down Rule technique used in EMMA applies to large-scale data sets in realistic organizational contexts. The results showed conclusively that EMMA attained the agreed success criteria for the evaluation and that the knowledge acquisition method used in EMMA outperforms standard machine learning methods.