Home > Research > Publications & Outputs > A randomized neural network for data streams

Electronic data

Links

Text available via DOI:

View graph of relations

A randomized neural network for data streams

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Close
Publication date19/05/2017
Host publicationProceedings of the 2017 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationUSA
PublisherIEEE
Pages3423-3430
Number of pages8
ISBN (electronic)9781509061822
ISBN (print)9781509061839
<mark>Original language</mark>English

Abstract

Randomized neural network (RNN) is a highly feasible solution in the era of big data because it offers a simple and fast working principle in processing dynamic and evolving data streams. This paper proposes a novel RNN, namely recurrent type-2 random vector functional link network (RT2McRVFLN), which provides a highly scalable solution for data streams in a strictly online and integrated framework. It is built upon the psychologically inspired concept of metacognitive learning, which covers three basic components of human learning: what-to-learn, how-to-learn, and when-to-learn. The what-to-learn selects important samples on the fly with the use of online active learning scenario, which renders our algorithm an online semi-supervised algorithm. The how-to-learn process combines an open structure of evolving concept and a randomized learning algorithm of random vector functional link network (RVFLN). The efficacy of the RT2McRVFLN has been numerically validated through two real-world case studies and comparisons with its counterparts, which arrive at a conclusive finding that our algorithm delivers a tradeoff between accuracy and simplicity.