Combining email models for false positive reduction

Machine learning and data mining can be effectively used to model, classify and discover interesting information for a wide variety of data including email. The Email Mining Toolkit, EMT, has been designed to provide a wide range of analyses for arbitrary email sources. Depending upon the task, one can usually achieve very high accuracy, but with some amount of false positive tradeoff. Generally false positives are prohibitively expensive in the real world. In the case of spam detection, for example, even if one email is misclassified, this may be unacceptable if it is a very important email. Much work has been done to improve specific algorithms for the task of detecting unwanted messages, but less work has been report on leveraging multiple algorithms and correlating models in this particular domain of email analysis. EMT has been updated with new correlation functions allowing the analyst to integrate a number of EMT's user behavior models available in the core technology. We ...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Shlomo Hershkop, Salvatore J. Stolfo
• Keywords: Arbitrary Email Sources, Data Mining, Email Mining Toolkit, KDD 2005, Large Email Data