A Randomized Neural Network for Data Streams

Randomized neural network (RNN) is a highly feasible approach in the era of big data because it offers a simple and fast working principle. The research issue in the current literature, however, lies in the capability of RNN 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 environment. 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 adopts a combination between evolving concept – flexible structural learning scenario and random vector functional link algorithm – solid basis for randomly generating network parameters. 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.