  Boosting is an ensemble method that combines base models in a sequential
manner to achieve high predictive accuracy. A popular learning algorithm based
on this ensemble method is eXtreme Gradient Boosting (XGB). We present an
adaptation of XGB for classification of evolving data streams. In this setting,
new data arrives over time and the relationship between the class and the
features may change in the process, thus exhibiting concept drift. The proposed
method creates new members of the ensemble from mini-batches of data as new
data becomes available. The maximum ensemble size is fixed, but learning does
not stop when this size is reached because the ensemble is updated on new data
to ensure consistency with the current concept. We also explore the use of
concept drift detection to trigger a mechanism to update the ensemble. We test
our method on real and synthetic data with concept drift and compare it against
batch-incremental and instance-incremental classification methods for data
streams.
