Tripartite Active Learning for Interactive Anomaly Discovery

Tripartite Active Learning for Interactive Anomaly Discovery

Most existing approaches to anomaly detection focus on statistical features of the data. However, in many cases, users are merely interested in a subset of the statistical outliers depending on the specific domain of interest, e.g., network attacks or financial fraud. The instruction from human expe...

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Bibliographic Details
Main Authors: Yanqiao Zhu, Kai Yang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Active learning
interactive artificial intelligence
anomaly detection
linear integer programming
human training
Online Access:https://ieeexplore.ieee.org/document/8707963/

Internet

https://ieeexplore.ieee.org/document/8707963/

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