  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 recover lost information in the discretisation
and new pruning techniques that may further reduce the size of rule models and
improve their accuracy. The proposed QCBA method was initially developed to
postprocess quantitative attributes in models generated by the Classification
based on associations (CBA) algorithm, but it can also be applied to the
results of other rule learning approaches. We demonstrate the effectiveness on
the postprocessing of models generated by five association rule classification
algorithms (CBA, CMAR, CPAR, IDS, SBRL) and two first-order logic rule learners
(FOIL2 and PRM). Benchmarks on 22 datasets from the UCI repository show smaller
size and the overall best predictive performance for FOIL2+QCBA compared to all
seven baselines. Postoptimised CBA models have a better predictive performance
compared to the state-of-the-art rule learner CORELS in this benchmark. The
article contains an ablation study for the individual postprocessing steps and
a scalability analysis on the KDD'99 Anomaly detection dataset.
