Interactive Anomaly Identification with Erroneous Feedback

The difficulties in analyzing large and extensive systems necessitate the use of efficient machine-learning tools to identify unknown system anomalies in order to avoid critical problems and ensure high reliability. Given that data logged by a system include unknown anomalies, anomaly identification...

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Bibliographic Details
Main Authors: Takaaki Tagawa, Yukihiro Tadokoro, Takehisa Yairi
Format: Article
Language:English
Published: The Prognostics and Health Management Society 2020-06-01
Series:International Journal of Prognostics and Health Management
Subjects:
Online Access:https://papers.phmsociety.org/index.php/ijphm/article/view/2924