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.

翻译：我们提出了一个通过数据抽象来监测分类系统的方法。数据抽象化依靠的是带有分辨率的框的概念。基于框的抽象化包括以每个维度的最小值和最大值代表一组数值。我们增加了含有分辨率概念的框,并定义了它们的集群覆盖范围,这是直观的量化指标,表明抽象质量。这样可以研究不同组群参数对已建框的影响,并估计一个亚最佳参数的间隔。此外,我们自动建立监测器,利用系统的正确和不正确行为。这样可以检查监测器的抽象性大小,分析网络的分离性。通过将系统每一类的子监测器合并到某些选定的层来获取监测器。我们的实验显示了我们分类包估计的有效性,并展示了如何根据选定的组群参数和监测层评估监测器的有效性和精确性。