An Incremental Learning approach using Long Short-Term Memory Neural Networks

Abstract

Due to Big Data and the Internet of Things, Machine Learning algorithms targeted specifically to model evolving data streams had gained attention from both academia and industry. Many Incremental Learning models had been successful in doing so, but most of them have one thing in common: they are complex variants of batch learning algorithms, which is a problem since, in a streaming setting, less complexity and more performance is desired. This paper proposes the Incremental LSTM model, which is a variant of the original LSTM with minor changes, that can tackle evolving data streams problems such as concept drift and the elasticity-plasticity dilemma without neither needing a dedicated drift detector nor a memory management system. It obtained great results that show it reacts fast to concept drifts and that is also robust to noise data.