Preference rules for label ranking: Mining patterns in multi-target relations

Cláudio Rebelo de Sá* (Corresponding Author), Paulo Azevedo, Carlos Soares, Alípio Mário Jorge, Arno Knobbe

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)


In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

Original languageEnglish
Pages (from-to)112-125
Number of pages14
JournalInformation Fusion
Publication statusPublished - 1 Mar 2018
Externally publishedYes


  • Association rules
  • Label ranking
  • Pairwise comparisons


Dive into the research topics of 'Preference rules for label ranking: Mining patterns in multi-target relations'. Together they form a unique fingerprint.

Cite this