What are the main approaches to anomaly detection?
mediumAnswer
- (1) Statistical: fit a distribution and flag low-density points (z-score, Gaussian mixtures).
- (2) Distance/density-based: LOF, DBSCAN.
- (3) Isolation-based: Isolation Forest — random splits, anomalies are isolated with few splits.
- (4) One-class SVM.
- (5) Reconstruction-based: autoencoders — high reconstruction error means anomaly.
- Choose based on data size, dimensionality, and whether you have any labels.
Check yourself — multiple choice
- Isolation Forest requires labeled anomalies
- Autoencoders flag anomalies via low reconstruction error
- Isolation Forest isolates anomalies faster than normal points
- One-class SVM is a supervised method
Isolation Forest exploits that anomalies get isolated in fewer random splits.
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