Publications:Interactive-cosmo : Consensus self-organized models for fault detection with expert feedback
From ISLAB/CAISR
| 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, WIDM 2019 |
| Conference | 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, Australia; 15 February, 2019 |
| 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. © 2019 Association for Computing Machinery. |
