Echo state hoeffding tree learning

Nowadays, real-time classification of Big Data streams is becoming essential in a variety of application domains. While decision trees are powerful and easy-to-deploy approaches for accurate and fast learning from data streams, they are unable to capture the strong temporal dependences typically present in the input data. Recurrent Neural Networks are an alternative solution that include an internal memory to capture these temporal depen- dences; however their training is computationally very expensive, with slow convergence and not easy-to-deploy (large number of hyper-parameters). Reservoir Computing was proposed to reduce the computation requirements of the training phase but still include a feed-forward layer which requires a large number of parameters to tune. In this work we propose a novel architecture for real-time classification based on the combination of a Reservoir and a decision tree. This combination makes classification fast, reduces the number of hyper-parameters and keeps the good temporal properties of recurrent neural networks. The capabilities of the proposed architecture to learn some typical string-based functions with strong temporal dependences are evaluated in the paper. The paper shows how the new architecture is able to incrementally learn these functions in real-time with fast adaptation to unknown sequences and analyzes the inuence of the reduced number of hyper-parameters in the behaviour of the proposed solution.