Understanding the reason why a prediction has been made by a machine is crucial to grant trust to a human decision-maker. However, data mining based decision support systems are, in general, not designed to promote interpretability; instead, they are developed to improve accuracy. Interpretability becomes a more challenging issue in the context of data stream mining. Where the prediction model has to deal with enormous volumes of data gathered continuously at a fast rate and whose underlying distribution may change over time. On the one hand, the majority of the methods that address classification in a data stream are black-box models or white-box models into ensembles. Either do not provide a clear view of why a particular decision has been made. On the other hand, white-box models, such as rule-based models, do not provide acceptable accuracy to be considered in many applications. This paper proposes modeling the data as a special graph, which is built over the attribute space, and from which interpretable rules can be extracted. To overcome concept drift and enhance model accuracy, different variants of such graphs are considered within an ensemble that is updated over time. The proposed approach has shown the best overall classification results when compared to six rule-based algorithms in twelve streaming domains.

了解机器做出预测的原因对于将信任授予人类决策者至关重要。但是，基于数据挖掘的决策支持系统通常并不是为了提高可解释性而设计的。相反，它们是为了提高准确性而开发的。在数据流挖掘的背景下，可解释性成为一个更具挑战性的问题。预测模型必须处理快速连续收集的大量数据，并且其基础分布可能会随时间变化。一方面，处理数据流中分类的大多数方法是将黑盒模型或白盒模型集成在一起。两者都没有清楚地说明为什么做出了特定的决定。另一方面，白盒模型，例如基于规则的模型，不能提供许多应用中要考虑的可接受的精度。本文提出将数据建模为一个特殊的图，该图建立在属性空间上，并且可以从中提取可解释的规则。为了克服概念漂移并提高模型准确性，可以在随时间更新的整体中考虑此类图的不同变体。与十二个流域中的六个基于规则的算法相比，该方法已显示出最佳的总体分类结果。