Abstract
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.
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Notes
- 1.
Available (with all Supplementary Material) at https://gitlab.com/chaver/choquet-rank.
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Vernerey, C., Aribi, N., Loudni, S., Lebbah, Y., Belmecheri, N. (2024). Learning to Rank Based on Choquet Integral: Application to Association Rules. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_25
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