document: 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 present results of combining classifier outputs for improving both accuracy and reducing false positives for the problem of spam detection . We apply these methods to a very large email data set and show results of different combination methods on these corpora . We introduce a new method to compare multiple and combined classifiers , and show how it differs from past work . The method analyzes the relative gain and maximum possible accuracy that can be achieved for certain combinations of classifiers to automatically choose the best combination .
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