  Big Data streams are being generated in a faster, bigger, and more
commonplace. In this scenario, Hoeffding Trees are an established method for
classification. Several extensions exist, including high-performing ensemble
setups such as online and leveraging bagging. Also, $k$-nearest neighbors is a
popular choice, with most extensions dealing with the inherent performance
limitations over a potentially-infinite stream.
  At the same time, gradient descent methods are becoming increasingly popular,
owing in part to the successes of deep learning. Although deep neural networks
can learn incrementally, they have so far proved too sensitive to
hyper-parameter options and initial conditions to be considered an effective
`off-the-shelf' data-streams solution.
  In this work, we look at combinations of Hoeffding-trees, nearest neighbour,
and gradient descent methods with a streaming preprocessing approach in the
form of a random feature functions filter for additional predictive power.
  We further extend the investigation to implementing methods on GPUs, which we
test on some large real-world datasets, and show the benefits of using GPUs for
data-stream learning due to their high scalability.
  Our empirical evaluation yields positive results for the novel approaches
that we experiment with, highlighting important issues, and shed light on
promising future directions in approaches to data-stream classification.
