5 Dealing with Change
Albert Bifet, Ricard Gavaldà, Geoffrey Holmes, Bernhard Pfahringer · 2018
A central feature of the data stream model is that streams evolve over time, and algorithms must react to the change. For example, let us consider email spam classifiers, which decide whether new incoming emails are or are not spam. As classifiers learn to improve their accuracy, spammers are going to modify their strategies to build spam messages, trying to fool the classifiers into believing they are not spam. Customer behavior prediction is another example: customers, actual or potential, change their preferences as prices rise or fall, as new products appear and others fall out of fashion, or simply as the time of the year changes. The predictors in these and other situations need to be adapted, revised, or replaced as time passes if they are to maintain reasonable accuracy.