Incorporating Expert Feedback into Active Anomaly Discovery

• Published in: 2016 IEEE 16th International Conference on Data Mining (ICDM) pp. 853 - 858
• Main Authors: Das, Shubhomoy, Weng-Keen Wong, Dietterich, Thomas, Fern, Alan, Emmott, Andrew
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: active learning; Algorithm design and analysis; Anomaly detection; Data models; Detection algorithms; Detectors; Feature extraction; Linear programming; Mathematical model; user feedback

Unsupervised anomaly detection algorithms search for outliers and then predict that these outliers are the anomalies. When deployed, however, these algorithms are often criticized for high false positive and high false negative rates. One cause of poor performance is that not all outliers are anomal...