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dc.contributor.authorIenco, Dino
dc.contributor.authorBifet, Albert
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorPoncelet, Pascal
dc.coverage.spatialGyeongju, Korea
dc.date.accessioned2015-06-12T02:18:29Z
dc.date.available2014
dc.date.available2015-06-12T02:18:29Z
dc.date.issued2014
dc.identifierhttp://dx.doi.org/10.1145/2554850.2554864
dc.identifier.citationIenco, D., Bifet, A., Pfahringer, B., & Poncelet, P. (2014). Change detection in categorical evolving data streams. In Procedings of 29th Annual ACM Symposium on Applied Computing, Gyeongju, Korea, March 24-28, 2014 (pp. 792–797). New York, NY, USA: ACM. http://doi.org/10.1145/2554850.2554864en
dc.identifier.isbn978-1-4503-2469-4
dc.identifier.urihttps://hdl.handle.net/10289/9405
dc.description.abstractDetecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution. To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream.
dc.format.extent792 - 797
dc.format.mimetypeapplication/pdf
dc.publisherACM
dc.rightsThis is an author’s accepted version of an article published in Procedings of 29th Annual ACM Symposium on Applied Computing. © 2015 ACM.
dc.sourceSAC'14
dc.subjectcategorical data
dc.subjectconcept drifts
dc.subjectevolving data stream
dc.subjectstatical test
dc.subjectunsupervised change detection
dc.subjectevolving data stream
dc.subjectcategorical data
dc.subjectunsupervised change detection
dc.subjectstatistical test
dc.subjectconcept drifts
dc.subjectMachine learning
dc.titleChange detection in categorical evolving data streams
dc.typeConference Contribution
dc.identifier.doi10.1145/2554850.2554864
dc.relation.isPartOfProcedings of 29th Annual ACM Symposium on Applied Computing
pubs.begin-page792
pubs.elements-id84887
pubs.end-page797


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