AB  - © 2017 IEEE. 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.
AU  - Pratama, M
AU  - Angelov, PP
AU  - Lu, J
AU  - Lughofer, E
AU  - Seera, M
AU  - Lim, CP
DA  - 2017/06/30
DO  - 10.1109/IJCNN.2017.7966286
EP  - 3430
JO  - Proceedings of the International Joint Conference on Neural Networks
PY  - 2017/06/30
SP  - 3423
TI  - A randomized neural network for data streams
VL  - 2017-May
Y1  - 2017/06/30
Y2  - 2026/05/23
ER  -