While the interpretability of machine learning models is often equated with their mere
syntactic comprehensibility, we think that interpretability goes beyond that, and that human …

It is conventional wisdom in machine learning and data mining that logical models such as
rule sets are more interpretable than other models, and that among such rule-based models …

This paper presents a novel approach for extracting the knowledge base (KB) of a Mamdani
fuzzy rule based system (FRBS) for stock market prediction. The KB, which is the most …

Classification approaches that allow to extract logical rules such as decision trees are often
considered to be more interpretable than neural networks. Also, logical rules are …

Mit dem durchschlagenden Erfolg von tiefen neuronalen Netzen (s. Abschnitt 11.3), die zwar
oft sehr genaue Vorhersagen liefern, aber keine unmittelbare Einsicht in die gelernten …

The increasing spread of artificial intelligence applications has led to decentralized
frameworks that foster collaborative model training among multiple entities. One of such …

Multi-label classification (MLC) is a supervised learning problem in which, contrary to
standard multiclass classification, an instance can be associated with several class labels …

In this vision paper, we argue that current solutions to data analytics are not suitable for
complex tasks from the humanities, as they are agnostic of the user and focused on static …

In his seminal paper, Mitchell has defined bias as “any basis for choosing one
generalization over another, other than strict consistency with the observed training …

After a brief review of the relationship between science and value, this paper introduces the
value of 'traditionality'as a value in the pure and applied sciences. Along with other …