Please use this identifier to cite or link to this item:

Sliding Reservoir Approach for Delayed Labeling in Streaming Data Classification

File Size Format  
paper0209.pdf 1.54 MB Adobe PDF View/Open

Item Summary Hu, Hanqing Kantardzic, Mehmed 2016-12-29T00:42:23Z 2016-12-29T00:42:23Z 2017-01-04
dc.identifier.isbn 978-0-9981331-0-2
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.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

Please email if you need this content in ADA-compliant format.

Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.