Towards better accuracy for Spam predictions
نویسنده
چکیده
Spam identification is crucial in implementing an effective email filtering system, while spam recognization has different properties comparing with normal text recognization. In this paper, we present three different classifiers with detailed analysis on various training data set of the given spam database. We then combine these classifiers into a mixture of expert system which yields overall better performance than any of individual contributors. We also give instructions for further improvements on classifiers as well as its requirement on spam databases.
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