MUSERNN: A Multilayer SelfEvolving Recurrent Neural Network for Data Stream Classification In this paper, we propose MUSERNN, a multilayer selfevolving recurrent neural network model for realtime classification of streaming data. Unlike the existing approaches, MUSERNN offers special treatment towards capturing temporal aspects of data stream through its novel recurrent learning approach based on the teacher forcing policy. Novelties here are twofold. First, in contrast to the traditional RNN models, MUSERNN has intrinsic ability to selfadjust its capacity by growing and pruning hidden nodes as well as layers, to handle the everchanging characteristics of data stream. Second, MUSERNN adopts a unique scoringbased layer adaptation mechanism, which makes it capable of recalling prior tasks, with minimum exploitation of network parameters. The performance of MUSERNN is evaluated in comparison with a number of stateoftheart techniques, using seven popular data streams and continual learning problems under prequential testthentrain protocol. Experimental results demonstrate the effectiveness of MUSERNN in stream classification scenario.

Streaming Random Patches for Evolving Data Stream Classification Ensemble methods are a popular choice for learning from evolving data streams. This popularity is due to (i) the ability to simulate simple, yet, successful ensemble learning strategies, such as bagging and random forests; (ii) the possibility of incorporating drift detection and recovery in conjunction to the ensemble algorithm; (iii) the availability of efficient incremental base learners, such as Hoeffding Trees. In this work, we introduce the Streaming Random Patches (SRP) algorithm, an ensemble method specially adapted to stream classification which combines random subspaces and online bagging. We provide theoretical insights and empirical results illustrating different aspects of SRP. In particular, we explain how the widely adopted incremental Hoeffding trees are not, in fact, unstable learners, unlike their batch counterparts, and how this fact significantly influences ensemble methods design and performance. We compare SRP against stateoftheart ensemble variants for streaming data in a multitude of datasets. The results show how SRP produce a high predictive performance for both real and synthetic datasets. Besides, we analyze the diversity over time and the average tree depth, which provides insights on the differences between local subspace randomization (as in random forest) and global subspace randomization (as in random subspaces).