In the context of tasks that highly involve human interaction and expert knowledge, ie,
operator guidance in manufacturing, the possibility of decision verifications by the user is a …

Automated prediction systems based on machine learning (ML) are employed in practical
applications with increasing frequency and stakeholders demand explanations of their …

Comprehending why an artificial intelligence–based agent makes certain predictions is
often discussed as one of the central issues that needs to be solved in the near future to …

Achieving at least some level of explainability requires complex analyses for many machine
learning systems, such as common black-box models. We recently proposed a new rule …

A very common and powerful step in the design process of a new learning algorithm or
extensions and improvements of existing algorithms is the benchmarking of models …

Learning classifier systems (LCSs) are a state-of-the-art methodology for develo** rule-
based machine learning by applying discovery algorithms and learning components. LCSs …

We explore variable-length metaheuristics for optimizing sets of rules for regression tasks by
extending an earlier short paper that performed a preliminary analysis of several variants of …

Rule-based machine learning (RBML) models are often presumed to be very beneficial for
tasks where explainabil-ity of machine learning models is considered essential. However …

To fill the increasing demand for explanations of decisions made by automated prediction
systems, machine learning (ML) techniques that produce inherently transparent models are …

We present a way to measure similarity between sets of rules for regression tasks. This was
identified to be an important but missing tool to investigate Metaheuristic Rule Set Learners …