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Title: Scalable teacher forcing network for semi-supervised large scale data streams
Authors: Pratama, Mahardhika
Za'in, Choiru
Lughofer, Edwin
Pardede, Eric
Rahayu, Dwi A. P.
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Pratama, M., Za'in, C., Lughofer, E., Pardede, E. & Rahayu, D. A. P. (2021). Scalable teacher forcing network for semi-supervised large scale data streams. Information Sciences, 576, 407-431.
Journal: Information Sciences
Abstract: The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction (DA3) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only 25% label proportions. It shows highly competitive performance even if compared with fully supervised learners with 100% label proportions.
ISSN: 0020-0255
DOI: 10.1016/j.ins.2021.06.075
Schools: School of Computer Science and Engineering 
Rights: © 2021 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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