  We present an approach for monitoring classification systems via data
abstraction. Data abstraction relies on the notion of box with a resolution.
Box-based abstraction consists in representing a set of values by its minimal
and maximal values in each dimension. We augment boxes with a notion of
resolution and define their clustering coverage, which is intuitively a
quantitative metric that indicates the abstraction quality. This allows
studying the effect of different clustering parameters on the constructed boxes
and estimating an interval of sub-optimal parameters. Moreover, we
automatically construct monitors that leverage both the correct and incorrect
behaviors of a system. This allows checking the size of the monitor
abstractions and analyzing the separability of the network. Monitors are
obtained by combining the sub-monitors of each class of the system placed at
some selected layers. Our experiments demonstrate the effectiveness of our
clustering coverage estimation and show how to assess the effectiveness and
precision of monitors according to the selected clustering parameter and
monitored layers.
