Abstract Concept drift refers to the phenomenon that the distribution generating the
observed data changes over time. If drift is present, machine learning models can become …

In an increasing number of industrial and technical processes, machine learning-based
systems are being entrusted with supervision tasks. While they have been successfully …

The world surrounding us is subject to constant change. These changes, frequently
described as concept drift, influence many industrial and technical processes. As they can …

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 …

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving
net-zero carbon emissions, and ensuring sustainable buildings and cities of the future …

Concept drift refers to a change in the data distribution affecting the data stream of future
samples. Consequently, learning models operating on the data stream might become …

Feature selection is one of the most relevant preprocessing and analysis techniques in
machine learning. It can dramatically increase the performance of learning algorithms and at …

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 …

As input data distributions evolve, the predictive performance of machine learning models
tends to deteriorate. In practice, new input data tend to come without target labels. Then …

Feature selection is one of the most relevant preprocessing and analysis techniques in
machine learning. It can dramatically increase the performance of learning algorithms and …