DYNG: Dynamic Online Growing Neural Gas for stream data classification.
Oliver Beyer, Philipp Cimiano · The European Symposium on Artificial Neural Networks · 2013
In this paper we introduce Dynamic Online Growing Neural Gas (DYNG), a novel online stream data classication approach based on Online Growing Neural Gas (OGNG). DYNG exploits labelled data during processing to adapt the network structure as well as the speed of growth of the network to the requirements of the classication task. It thus speeds up learning for new classes/labels and dampens growth of the subnetwork representing the class once the class error converges. We show that this strategy is benecial in life-long learning settings involving non-stationary data, giving DYNG an increased performance in highly non-stationary phases compared to OGNG.