Anomaly Innovation: Platforms, Results, Functions

Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly „alarms“ to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning.

See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
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Outliers – How to detect the outliers and reduce the effect using variable transformation like using log, square root, cube root or other suitable method.
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