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

• Published in: Information fusion 2018-03, Vol.40, p.112-125
• Main Authors: de Sá, Cláudio Rebelo, Azevedo, Paulo, Soares, Carlos, Jorge, Alípio Mário, Knobbe, Arno
• Format: Article
• Language: English
• Subjects: Association rules; Label ranking; Pairwise comparisons
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2017.07.001

•A sensitivity analysis is carried out to test Label Ranking Association Rules.•Similarity-based interest measures contribute positively to the accuracy of the model.•Results indicate which type of datsets benefit more from the methodology.•Pairwise Association Rules are proposed.•New rules proposed are effective in the discovery of relations in ranking data. 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.