Detecting Spam using Machine Learning
Strictly speaking, "one class classification" does not make sense as an idea. If there is only one possible state of a predicted value, then there is no prediction problem. The answer is always the single class.

Concretely, if you only have spam examples, you would always achieve 100% accuracy by classifying all email as spam. This is clearly wrong, and the only way to know how it is wrong is to know where the classification is wrong -- where emails are not in the spam class.

So-called one-class classification techniques are really anomaly detection approaches. They have an implicit assumption that things unlike the examples are not part of the single class, but, this is just an assumption about data being probably not within the class. There's a binary classification problem lurking in there.

What is wrong with a binary classifier?