An Anti-spam Filter Combination Framework for Text-and-Image Emails through Incremental Learning
نویسندگان
چکیده
We present an anti-spam filtering framework that combines text-based and image-based anti-spam filters. First, an incremental learning approach to reducing mismatches between training and test datasets is proposed to resolve the problem of a lack of training data for legitimate emails that contain both text and images. Then, the outputs of text-based and image-based filters are combined with the weights determined by a Bayesian framework. Our experimental results on the TREC 2005 and 2007 spam corpora using two state-of-theart text-based filters show that the combined system significantly reduces the false positive errors for the misclassified emails containing images.
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