Subgroup discovery is a broadly applicable descriptive data mining technique for identifying
interesting subgroups according to some property of interest. This article summarizes …

In this work, we propose a simple but effective method to interpret black-box machine
learning models globally. That is, we use a compact binary tree, the interpretation tree, to …

Complex human activity recognition (CHAR) remains a pivotal challenge within ubiquitous
computing, especially in the context of smart environments. Existing studies typically require …

Mining ubiquitous sensing data is important but also challenging, due to many factors, such
as heterogeneous large-scale data that is often at various levels of abstraction. This also …

Conventionally, event-related potential (ERP) analysis relies on the researcher to identify
the sensors and time points where an effect is expected. However, this approach is prone to …

Computational sensemaking aims to develop methods and systems to “make sense” of
complex data and information. The ultimate goal is then to provide insights and enhance …

The three volume set LNAI 9851, LNAI 9852, and LNAI 9853 constitutes the refereed
proceedings of the European Conference on Machine Learning and Knowledge Discovery …

Social media and social networks have already woven themselves into the very fabric of
everyday life. This results in a dramatic increase of social data capturing various relations …

Explainable AI has received a lot of attention over the past decade, with the proposal of
many methods explaining black box classifiers such as neural networks. Despite the …

Computational social sensing is enabled by the Internet of Things at large scale. Using
sensors, eg, implemented in mobile and wearable devices, human behavior and activities …