Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/57066
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dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorKittawit Autchariyapanitkulen_US
dc.contributor.authorOlga Koshelevaen_US
dc.contributor.authorVladik Kreinovichen_US
dc.date.accessioned2018-09-05T03:34:31Z-
dc.date.available2018-09-05T03:34:31Z-
dc.date.issued2017-08-30en_US
dc.identifier.other2-s2.0-85030837585en_US
dc.identifier.other10.1109/IFSA-SCIS.2017.8023348en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85030837585&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/57066-
dc.description.abstract© 2017 IEEE. In many practical situations, we need to fuse and integrate information and knowledge from different sources - and do it under uncertainty. Most existing methods for information fusion and knowledge integration take into account uncertainty. In addition to uncertainty, we also face the problem of reliability: sensors may malfunction, experts can be wrong, etc. In this paper, we show how to take into account both uncertainty and reliability in information fusion and knowledge integration. We show this on the examples of probabilistic and fuzzy uncertainty.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleUncertain information fusion and knowledge integration: How to take reliability into accounten_US
dc.typeConference Proceedingen_US
article.title.sourcetitleIFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systemsen_US
article.stream.affiliationsNew Mexico State University Las Crucesen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsMaejo Universityen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
Appears in Collections:CMUL: Journal Articles

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