|Authors||N. Belmecheri, A. Noureddine, N. Lazaar, Y. Lebbah and S. Loudni|
|Title||Boosting the Learning for Ranking Patterns|
|Project(s)||AI4CCAM: Trustworthy AI for Cooperative, Connected & Automated Mobility|
|Publication Type||Journal Article|
|Year of Publication||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.