Anomaly detection is a useful machine learning technique for identifying interesting, valuable or unusual instances in data sets. Applications for anomaly detection are diverse, including: fraud and counterfeit detection; surveillance; network, security and process monitoring; data exploration and more.
In this presentation, I review the basic ideas behind outlier based detectors, and compare this to traditional classification. I highlight practical and advanced issues for performance. Finally, I present an application of anomaly detection for detecting seizures from intracranial EEG time series.
See the accompanying video, http://vimeo.com/71931374