An Evolving Population Approach to Data-Stream Classification with
  Extreme Verification Latency
Conor Fahy Shengxiang Yang
Recognising and reacting to change in non-stationary data-streams is a
challenging task. The majority of research in this area assumes that the true
class label of incoming points are available, either at each time step or
intermittently with some latency. In the worse case this latency approaches
infinity and we can assume that no labels are available beyond the initial
training set. When change is expected and no further training labels are
provided the challenge of maintaining a high classification accuracy is very
great. The challenge is to propagate the original training information through
several timesteps, possibly indefinitely, while adapting to underlying change
in the data-stream. In this paper we conduct an initial study into the
effectiveness of using an evolving, population-based approach as the mechanism
for adapting to change. An ensemble of one-class-classifiers is maintained for
each class. Each classifier is considered as an agent in the sub-population and
is subject to selection pressure to find interesting areas of the feature
space. This selection pressure forces the ensemble to adapt to the underlying
change in the data-stream.