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Fault-tolerant incremental diagnosis with limited historical data
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0001-8952-3542
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0002-5893-7774
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0001-8577-6745
2008 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We describe a novel incremental diagnostic system based on a statistical model that is trained from empirical data. The system guides the user by calculating what additional information would be most helpful for the diagnosis. We show that our diagnostic system can produce satisfactory classification rates, using only small amounts of available background information, such that the need of collecting vast quantities of initial training data is reduced. Further, we show that incorporation of inconsistency-checking mechanisms in our diagnostic system reduces the number of incorrect diagnoses caused by erroneous input.

Place, publisher, year, edition, pages
2008, 1. , p. 8
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-23493OAI: oai:DiVA.org:ri-23493DiVA, id: diva2:1042569
Conference
International Conference on Prognostics and Health Management 2008 (PHM'08), 6-9 October 2008, Denver, Colorado, USA
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved

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Steinert, RebeccaHolst, Anders

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