Interpretable regularized class association rules algorithm for classification in a categorical data space

Mohamed Azmi, George C. Runger, Abdelaziz Berrado

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Using association rules in classification is a great success which produces high accuracy classifiers. Even so, the principal advantage of the associative classifiers lies in interpretation. However, pruning the useless rules among the huge set of the mined rules as well as combining them to build a classifier remains a subject for improvement and further research. In this paper, we introduce a new algorithm to build a classifier based on Regularized Class Association Rules in a categorical data space called RCAR. The characteristic of this algorithm is, therefore, threefold: First, mining an exhaustive set of Class Association Rules (CARs) according to a predefined values of support and confidence thresholds. Second, applying a regularized logistic regression algorithm with Lasso penalty on the rules space to build a model that predicts the conditional probability of the existence of the outcome. Useless rules are pruned thanks to the selective nature of Lasso regularization. Third, organizing and visualizing the CARs which survive the first step of pruning by Lasso regularization using metarules. An optional step of pruning could be undertaken on the basis of the metarules and subject knowledge. Likewise, the empirical results indicate that RCAR gives comparable accuracy against Random Forest and GBM.

Original languageEnglish (US)
Pages (from-to)313-331
Number of pages19
JournalInformation Sciences
StatePublished - May 2019


  • Association rules
  • Class association rules
  • Classification
  • Ensemble learning
  • Pruning
  • Regularization

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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