@proceedings{17533,
  author = {Charles Vernerey and Noureddine Aribi and Yahia Lebbah and Samir Loudni and Nassim Belmecheri},
  title = {Learning to Rank Based on Choquet Integral: Application to Association Rules},
  abstract = {Discovering relevant patterns for a particular user remains a challenging tasks in data mining. One way to come with this difficulty is to use interestingness measures to create a ranking. Although these measures allow evaluating patterns from various sights, yet they may generate different rankings and hence highlight different understandings of what a good pattern. In this paper, we investigate the potential of learning-to-rank techniques to learn to rank directly. We use the Choquet integral, which belongs to family of non-linear aggregators, to learn an aggregation function from user{\textquoteright}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 genes expression data.},
  year = {2024},
  journal = {PAKDD},
  month = {2024},
}