Anomaly Detection v.s. Supervised Learning

Anomaly Detection

Fraud detection, manufacturing, Monitoring machines in data center

• Very small number of positive examples(y=1).(0-20 is common)
• Large number of negative(y=0) examples.
• Many different "types" of anomalies. Hard for any algorithm to learn from positive examples what the anomalies look like.
• Future anomalies may look nothing like any of the anomalous examples we've seen so far.

Supervised Learning
Email spam, weather prediction, cancer classification.

• Large number of positive and negative examples.
• Enough positive examples for algorithm to get a sense of what positive example are like, future positive examples likely to be similar to ones in training set.