QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisation

TL;DR

The paper introduces QCBA, a postprocessing method that enhances rule classifiers from quantitative data by recovering information lost during discretisation, leading to more accurate and compact models.

Contribution

It proposes new rule tuning and pruning techniques, applicable across various rule learning algorithms, to improve model accuracy and reduce size.

Findings

QCBA improves model accuracy across multiple algorithms.

Postprocessed models are smaller and more interpretable.

FOIL2+QCBA outperforms baselines on UCI datasets.

Abstract

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.