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Sliding Reservoir Approach for Delayed Labeling in Streaming Data Classification

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Title:Sliding Reservoir Approach for Delayed Labeling in Streaming Data Classification
Authors:Hu, Hanqing
Kantardzic, Mehmed
Keywords:delayed labeling
streaming data mining
concept drift
semi-supervised learning
Date Issued:04 Jan 2017
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.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Streaming Data Analytics and Applications Minitrack

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