Preference rules for label ranking: Mining patterns in multi-target relations
This work addresses the problem of mining patterns in multi-target preference data for researchers in data mining and machine learning, but it is incremental as it builds on existing association rule methods.
The paper investigates two variants of association rules for preference data, Label Ranking Association Rules (LRAR) and Pairwise Association Rules (PAR), to handle multi-target relations in label ranking tasks, with experimental results demonstrating their potential.
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.