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dc.contributor.authorAbdulsalam, Hanady
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2008-07-15 16:12:33.221en
dc.date.accessioned2008-07-16T17:14:17Z
dc.date.available2008-07-16T17:14:17Z
dc.date.issued2008-07-16T17:14:17Z
dc.identifier.urihttp://hdl.handle.net/1974/1321
dc.descriptionThesis (Ph.D, Computing) -- Queen's University, 2008-07-15 16:12:33.221en
dc.description.abstractRecent research addresses the problem of data-stream mining to deal with applications that require processing huge amounts of data such as sensor data analysis and financial applications. Data-stream mining algorithms incorporate special provisions to meet the requirements of stream-management systems, that is stream algorithms must be online and incremental, processing each data record only once (or few times); adaptive to distribution changes; and fast enough to accommodate high arrival rates. We consider the problem of data-stream classification, introducing an online and incremental stream-classification ensemble algorithm, Streaming Random Forests, an extension of the Random Forests algorithm by Breiman, which is a standard classification algorithm. Our algorithm is designed to handle multi-class classification problems. It is able to deal with data streams having an evolving nature and a random arrival rate of training/test data records. The algorithm, in addition, automatically adjusts its parameters based on the data seen so far. Experimental results on real and synthetic data demonstrate that the algorithm gives a successful behavior. Without losing classification accuracy, our algorithm is able to handle multi-class problems for which the underlying class boundaries drift, and handle the case when blocks of training records are not big enough to build/update the classification model.en
dc.format.extent1183066 bytes
dc.format.mimetypeapplication/pdf
dc.languageenen
dc.language.isoenen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectData miningen
dc.subjectStreamsen
dc.subjectClassification algorithmsen
dc.subjectStreaming algorithmsen
dc.titleStreaming Random Forestsen
dc.typeThesisen
dc.description.degreePh.Den
dc.contributor.supervisorMartin, Patricken
dc.contributor.supervisorSkillicorn, B. Daviden
dc.contributor.departmentComputingen


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