Anomaly detection
Anomaly detection is a specific approach to detecting rare events, where the focus is on identifying instances that significantly deviate from the norm or normal behavior. Anomalies can be caused by rare events, errors, or unusual patterns that are not typical in the dataset. This technique is particularly useful when there is limited or no labeled data for rare events. Common anomaly detection algorithms include the following:
- Unsupervised methods: Techniques such as Isolation Forest and One-Class SVM) can be used to identify anomalies in data without requiring labeled examples of the rare event.
- Semi-supervised methods: These approaches combine normal and abnormal data during training but have only a limited number of labeled anomalies. Autoencoders and variational autoencoders are examples of semi-supervised anomaly detection algorithms.
- Supervised methods: If a small number of labeled anomalies are available, supervised learning algorithms such...
