Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

The utilization of surrogate models to approximate complex systems has recently gained
increased popularity. Because of their capability to deal with black-box problems and lower …

Flowing waters have a unique role in supporting global biodiversity, biogeochemical cycles
and human societies,,,–. Although the importance of permanent watercourses is well …

We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …

The performance of machine learning algorithms are affected by several factors, some of
these factors are related to data quantity, quality, or its features. Another element is the …

Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Modern supervised machine learning algorithms involve hyperparameters that have to be
set before running them. Options for setting hyperparameters are default values from the …

Intrusion detection systems (IDSs) play a pivotal role in computer security by discovering
and repealing malicious activities in computer networks. Anomaly-based IDS, in particular …