Incremental Extreme Learning Machine for Binary Data Stream Classification

Classifier ensembles have shown the ability to classify drifted data streams. The following paper proposes an ensemble consisting of a single hidden layer feedforward neural network and an Extreme Learning Machine. For this purpose, a new incremental version of the Extreme Learning Machine is also proposed. Motivations behind such an approach have been precisely described and supported by conducted research. The achieved results show when the architecture might be most useful and what are the possible directions for future development of this method.