Learning to Rank Based on Choquet Integral: Application to Association Rules

Discovering relevant patterns for a particular user remains a challenging data mining task. One way to deal with this difficulty is to use interestingness measures to create a ranking. Although these measures allow evaluating patterns from various sights, they may generate different rankings and hence highlight different understandings of what a good pattern is. This paper investigates the potential of learning-to-rank techniques to learn to rank directly. We use the Choquet integral, which belongs to the family of non-linear aggregators, to learn an aggregation function from the user’s feedback. We show the interest of our approach on association rules, whose added-value is studied on UCI datasets and a case study related to the analysis of gene expression data.