AuthorsN. Belmecheri, A. Noureddine, N. Lazaar, Y. Lebbah and S. Loudni
TitleBoosting the Learning for Ranking Patterns
AfilliationSoftware Engineering
Project(s)AI4CCAM: Trustworthy AI for Cooperative, Connected & Automated Mobility
Publication TypeJournal Article
Year of Publication2023
Date Published04/2023

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.

Citation Key43214

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