Parsimonious Random Vector Functional Link Network for Data Streams

Pratama, Mahardhika and Angelov, Plamen P. and Lughofer, Edwin and Joo Er, Meng (2018) Parsimonious Random Vector Functional Link Network for Data Streams. Information Sciences, 430-43. pp. 519-537. ISSN 0020-0255

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The majority of the existing work on random vector functional link networks (RVFLNs) is not scalable for data stream analytics because they work under a batch learning scenario and lack a self-organizing property. A novel RVLFN, namely the parsimonious random vector functional link network (pRVFLN), is proposed in this paper. pRVFLN adopts a fully flexible and adaptive working principle where its network structure can be configured from scratch and can be automatically generated, pruned and recalled from data streams. pRVFLN is capable of selecting and deselecting input attributes on the fly as well as capable of extracting important training samples for model updates. In addition, pRVFLN introduces a non-parametric type of hidden node which completely reflects the real data distribution and is not constrained by a specific shape of the cluster. All learning procedures of pRVFLN follow a strictly single-pass learning mode, which is applicable for online time-critical applications. The advantage of pRVFLN is verified through numerous simulations with real-world data streams. It was benchmarked against recently published algorithms where it demonstrated comparable and even higher predictive accuracies while imposing the lowest complexities.

Item Type:
Journal Article
Journal or Publication Title:
Information Sciences
Additional Information:
This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 430-431, 2018 DOI: 10.1016/j.ins.2017.11.050
Uncontrolled Keywords:
?? random vector functional linkevolving intelligent systemonline learningonline identificationrandomized neural networksartificial intelligencetheoretical computer sciencesoftwareinformation systems and managementcontrol and systems engineeringcomputer scie ??
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Deposited On:
04 Dec 2017 09:28
Last Modified:
16 Jul 2024 23:52