  In machine learning, the one-class classification problem occurs when
training instances are only available from one class. It has been observed that
making use of this class's structure, or its different contexts, may improve
one-class classifier performance. Although this observation has been
demonstrated for static data, a rigorous application of the idea within the
data stream environment is lacking. To address this gap, we propose the use of
context to guide one-class classifier learning in data streams, paying
particular attention to the challenges presented by the dynamic learning
environment. We present three frameworks that learn contexts and conduct
experiments with synthetic and benchmark data streams. We conclude that the
paradigm of contexts in data streams can be used to improve the performance of
streaming one-class classifiers.
