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Article Dans Une Revue Algorithms Année : 2023

Boosting the Learning for Ranking Patterns

Résumé

Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multi-criteria decision-making problem. Our approach uses an analytic hierarchy process (AHP) to elicit weights for different interestingness measures based on user preference. We also propose an active learning mode with a sensitivity-based heuristic to minimize user ranking queries while still providing high-quality results. Experiments show that our approach significantly reduces running time and returns precise pattern ranking while being robust to user mistakes, compared to state-of-the-art approaches.
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Dates et versions

hal-04130835 , version 1 (11-10-2023)

Identifiants

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Nassim Belmecheri, Noureddine Aribi, Nadjib Lazaar, Yahia Lebbah, Samir Loudni. Boosting the Learning for Ranking Patterns. Algorithms, 2023, 16 (5), pp.218. ⟨10.3390/a16050218⟩. ⟨hal-04130835⟩
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