Learning Simplified Decision Boundaries from Trapezoidal Data Streams

We present a novel adaptive feedforward neural network for online learning from doubly-streaming data, where both the data volume and feature space grow simultaneously. Traditional online learning and feature selection algorithms can't handle this problem because they assume that the feature space of the data stream remains unchanged. We propose a Single Hidden Layer Feedforward Neural Network with Shortcut Connections (SLFN-S) that learns if a data stream needs to be mapped using a non-linear transformation or not, to speed up the learning convergence. We employ a growing strategy to adjust the model complexity to the continuously changing feature space. Finally, we use a weight-based pruning procedure to keep the run time complexity of the proposed model linear in the size of the input feature space, for efficient learning from data streams. Experiments with trapezoidal data streams on 8 UCI datasets were conducted to examine the performance of the proposed model. We show that SLFN-S outperforms the state of the art learning algorithm from trapezoidal data streams [16].