Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41358

Sliding Reservoir Approach for Delayed Labeling in Streaming Data Classification

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dc.contributor.author Hu, Hanqing
dc.contributor.author Kantardzic, Mehmed
dc.date.accessioned 2016-12-29T00:42:23Z
dc.date.available 2016-12-29T00:42:23Z
dc.date.issued 2017-01-04
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41358
dc.description.abstract When concept drift occurs within streaming data, a streaming data classification framework needs to update the learning model to maintain its performance. Labeled samples required for training a new model are often unavailable immediately in real world applications. This delay of labels might negatively impact the performance of traditional streaming data classification frameworks. To solve this problem, we propose Sliding Reservoir Approach for Delayed Labeling (SRADL). By combining chunk based semi-supervised learning with a novel approach to manage labeled data, SRADL does not need to wait for the labeling process to finish before updating the learning model. Experiments with two delayed-label scenarios show that SRADL improves prediction performance over the naïve approach by as much as 7.5% in certain cases. The most gain comes from 18-chunk labeling delay time with continuous labeling delivery scenario in real world data experiments.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject delayed labeling
dc.subject streaming data mining
dc.subject concept drift
dc.subject semi-supervised learning
dc.subject classification
dc.title Sliding Reservoir Approach for Delayed Labeling in Streaming Data Classification
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2017.205
Appears in Collections: Streaming Data Analytics and Applications Minitrack


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