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Anomaly Detection in Machine Learning: Finding What Doesn’t Belong

  • Anomaly detection in machine learning involves identifying unusual data points that don't conform to expected patterns, such as in bank transaction alerts.
  • It plays a critical role in various scenarios like monitoring system performance, fraud detection, and predictive maintenance.
  • Approaches include supervised learning (using labeled data), unsupervised learning (detecting anomalies in mostly normal data), and semi-supervised learning (training on normal data).
  • Popular ML algorithms for anomaly detection include Isolation Forest, One-Class SVM, Autoencoders, and LSTM for time series data.

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