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dc.contributor.authorBifet, Alberten_NZ
dc.contributor.authorZhang, Jiajinen_NZ
dc.contributor.authorFan, Weien_NZ
dc.contributor.authorHe, Chengen_NZ
dc.contributor.authorZhang, Jianfengen_NZ
dc.contributor.authorQian, Jianfengen_NZ
dc.contributor.authorHolmes, Geoffreyen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.coverage.spatialConference held Halifax, NX, Canadaen_NZ
dc.date.accessioned2018-01-08T02:06:09Z
dc.date.available2017en_NZ
dc.date.available2018-01-08T02:06:09Z
dc.date.issued2017en_NZ
dc.identifier.citationBifet, A., Zhang, J., Fan, W., He, C., Zhang, J., Qian, J., … Pfahringer, B. (2017). Extremely fast decision tree mining for evolving data streams. In Proceedings of 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1733–1742). New York, USA: ACM. https://doi.org/10.1145/3097983.3098139en
dc.identifier.isbn978-1-4503-4887-4en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/11588
dc.description.abstractNowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning. In this paper, we present a new system called STREAMDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. STREAMDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision trees. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherACMen_NZ
dc.rights© 2017 Copyright held by the author(s). Publication rights licensed to Association for Computing Machinery.
dc.sourceKDD '17en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectclassificationen_NZ
dc.subjectdata streamsen_NZ
dc.subjectdecision treesen_NZ
dc.subjectonline learningen_NZ
dc.titleExtremely fast decision tree mining for evolving data streamsen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1145/3097983.3098139en_NZ
dc.relation.isPartOfProceedings of 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Miningen_NZ
pubs.begin-page1733
pubs.elements-id203609
pubs.end-page1742
pubs.finish-date2017-08-17en_NZ
pubs.place-of-publicationNew York, USA
pubs.start-date2017-08-13en_NZ


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