138 citations · 237 across the 5 of their papers we have counts for
5 papers
Neurosymbolic Methods for Rule Mining
Agnieszka Lawrynowicz, Luis Galarraga, Mehwish Alam +3
In this chapter, we address the problem of rule mining, beginning with essential background information, including measures of rule quality. We then explore various rule mining met…
Advances in Machine Learning for the Behavioral Sciences
Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz
The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as clas…
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models
Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz
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…
On Cognitive Preferences and the Plausibility of Rule-based Models
Johannes Fürnkranz, Tomáš Kliegr, Heiko Paulheim
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,…
QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisation
Tomas Kliegr, Ebroul Izquierdo
A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to…