Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data

As machine learning is increasingly applied in an online fashion to deal with evolving data streams, the fairness of these algorithms is a matter of growing ethical and legal concern. In many use cases, class imbalance in the data also needs to be dealt with to ensure predictive performance. Current fairness-aware stream learners typically attempt to solve these issues through in- or post-processing by focusing on optimizing one specific discrimination metric, addressing class imbalance in a separate processing step. While C-SMOTE is a highly effective model-agnostic pre-processing approach to mitigate class imbalance, as a side effect of this method, algorithmic bias is often introduced. Therefore, we propose CFSMOTE - a fairness-aware, continuous SMOTE variant - as a pre-processing approach to simultaneously address the class imbalance and fairness concerns by employing situation testing and balancing fairness-relevant groups during oversampling. Unlike other fairness-aware stream learners, CFSMOTE is not optimizing for only one specific fairness metric, therefore avoiding potentially problematic trade-offs. Our experiments show significant improvement on several common group fairness metrics in comparison to vanilla C-SMOTE while maintaining competitive performance, also in comparison to other fairness-aware algorithms.

Localization of Small Leakages in Water Distribution Networks using Concept Drift Explanation Methods

Facing climate change the already limited availability of drinking water will decrease in the future rendering drinking water an increasingly scarce resource. Considerable amounts of it are lost through leakages in water transportation and distribution networks. Leakage detection and localization are challenging problems due to the complex interactions and changing demands in water distribution networks. Especially small leakages are hard to pinpoint yet their localization is vital to avoid water loss over long periods of time. While there exist different approaches to solving the tasks of leakage detection and localization, they are relying on various information about the system, e.g. real-time demand measurements and the precise network topology, which is an unrealistic assumption in many real-world scenarios. In contrast, this work attempts leakage localization using pressure measurements only. For this purpose, first, leakages in the water distribution network are modeled employing Bayesian networks, and the system dynamics are analyzed. We then show how the problem is connected to and can be considered through the lens of concept drift. In particular, we argue that model-based explanations of concept drift are a promising tool for localizing leakages given limited information about the network. The methodology is experimentally evaluated using realistic benchmark scenarios.