In this chapter, we examined anomalies in data. We discussed several approaches to anomaly detection and looked at two kinds of anomalies: outliers and novelties. We considered the fact that anomaly detection is primarily an unsupervised learning problem, but despite this, some algorithms require labeled data, while others are semi-supervised. The reason for this is that, generally, there is a tiny number of positive examples (that is, anomalous samples) and a large number of negative examples (that is, standard samples) in anomaly detection tasks.
In other words, we usually don't have enough positive samples to train algorithms. That is why some solutions use labeled data to improve algorithm generalization and precision. On the contrary, supervised learning usually requires a large number of positive and negative examples, and their distribution needs to be balanced...
