Learn to filter spam

Almost everyday we receive lots of spam emails. Training classifiers to filter spam is one of the most successful real-life applications of machine learning. But spammers are getting smart to counteract spam filters! One trick they use is good word attack. By inserting a set of words that often appear in legitimate messages but not in spams, they make spams look legitimate and confuse the filters (increasing false negative). Even worse, when end users label such emails as spams, adaptive spam filters will learn from these new training samples, and may associate those good words with spam (increasing false positive). These days we do sometimes receive such emails, don't we?

So this paper, "A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters" (JMLR Jun 08), proposed to use multi-instance learning for spam filters. Multi-instance learning learns from and makes predictions on bags of instances, instead of individual instances. If at least one of the instances in a bag is positive, then the bag is positive; otherwise, the bag is negative. Let a bag be an email, and an instance be a part of the email. You see this is a perfect match for good word attack. Now I'm wondering what spammers will do next....