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stat.ML2018★ 138 cited
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…
stat.ML2017★ 8 cited
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…