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dc.contributor.authorAbdolsamadi, Amirmahyar
dc.contributor.authorWang, Pingfeng
dc.date.accessioned2018-02-16T15:57:02Z
dc.date.available2018-02-16T15:57:02Z
dc.date.issued2017
dc.identifier.citationAbdolsamadi A, Wang P. Concept Drift and Evolution Detection in Fusion Diagnosis With Evolving Data Streams. ASME. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 2A: 43rd Design Automation Conference ():V02AT03A046. doi:10.1115/DETC2017-68373en_US
dc.identifier.isbn978-0-7918-5812-7
dc.identifier.otherWOS:000423243800046
dc.identifier.urihttp://dx.doi.org/10.1115/DETC2017-68373
dc.identifier.urihttp://hdl.handle.net/10057/14558
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractHealth diagnosis interprets data streams acquired by smart sensors and makes inferences about health conditions of an engineering system thereby making critical operational decisions. A data stream is a flow of continuous data that face some challenges in data mining. This paper addresses concept drift and concept evolution as two major challenges in the classification of streaming data. Concept drift occurs as a result of data distribution changes. Concept evolution happens when new classes appear in the stream. These changes may cause the degradation of classification results over time. This paper presents an adaptive fusion learning approach to build a robust classification model. The proposed approach consists of three steps: (i) proposed fusion formulation using weighted majority voting (ii) active learning to labels selectively instead of querying for all true labels (iii) distance-based approach to monitoring the movement of data distribution. A diagnosis case study has been used to demonstrate the developed fusion diagnosis methodology.en_US
dc.description.sponsorshipNational Science Foundation through Faculty Early Career Development (CAREER) award [CMMI-1351414, CMMI-1200597]; Department of Transportation through University Transportation Center (UTC) Program.en_US
dc.language.isoen_USen_US
dc.publisherASMEen_US
dc.relation.ispartofseriesASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference;v.2A
dc.subjectFlow (Dynamics)en_US
dc.subjectSensorsen_US
dc.subjectEngineering systems and industry applicationsen_US
dc.subjectData miningen_US
dc.titleConcept drift and evolution detection in fusion diagnosis with evolving data streamsen_US
dc.typeConference paperen_US
dc.rights.holderCopyright © 2017 by ASMEen_US


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