FERNN: A Fast and Evolving Recurrent Neural Network Model for Streaming Data Classification

With the recent explosion of data navigating in motion, there is a growing research interest for analyzing streaming data, and consequently, there are several recent works on data stream analytics. However, exploring the potentials of traditional recurrent neural network (RNN) in the context of streaming data classification is still a little investigated area. In this paper, we propose a novel variant of RNN, termed as FERNN, which features single-pass learning capability along with self-evolution property. The online learning capability makes FERNN fit for working on streaming data, whereas the self-organizing property makes the model adaptive to the rapidly changing environment. FERNN utilizes hyperplane activation in the hidden layer, which not only reduces the network parameters to a significant extent, but also triggers the model to work by default as per teacher forcing mechanism so that it automatically handles the vanishing/exploding gradient issues in traditional RNN learning based on back-propagation-through-time policy. Moreover, unlike the majority of the existing autonomous learning models, FERNN is free from normal distribution assumption for streaming data, making it more flexible. The efficacy of FERNN is evaluated in terms of classifying six publicly available data streams, under the prequential test-then-train protocol. Experimental results show encouraging performance of FERNN attaining state-of-the-art classification accuracy with fairly reduced computation cost.