Publications:Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback

From ISLAB/CAISR
Revision as of 22:22, 10 January 2020 by Slawek (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Do not edit this section

Keep all hand-made modifications below

Title Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback
Author Ece Calikus and Yuantao Fan and Sławomir Nowaczyk and Anita Sant'Anna
Year 2019
PublicationType Conference Paper
Journal
HostPublication Proceedings of the Workshop on Interactive Data Mining. ACM, 2019.
Conference Workshop on Interactive Data Mining, The International Conference on Information and Knowledge Management
DOI http://dx.doi.org/10.1145/3304079.3310289
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1384810
Abstract Diagnosing deviations and predicting faults is an important task, especially given recent advances related to Internet of Things. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. One promising approach towards self-monitoring systems is based on the "wisdom of the crowd" idea, where malfunctioning equipments are detected by understanding the similarities and differences in the operation of several alike systems.A fully autonomous fault detection, however, is not possible, since not all deviations or anomalies correspond to faulty behaviors; many can be explained by atypical usage or varying external conditions. In this work, we propose a method which gradually incorporates expert-provided feedback for more accurate self-monitoring. Our idea is to support model adaptation while allowing human feedback to persist over changes in data distribution, such as concept drift.