Authors | N. Belmecheri, A. Noureddine, N. Lazaar, Y. Lebbah and S. Loudni |
Title | Boosting the Learning for Ranking Patterns |
Afilliation | Software Engineering |
Project(s) | AI4CCAM: Trustworthy AI for Cooperative, Connected & Automated Mobility |
Status | Published |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Algorithms |
Volume | 16 |
Issue | 5 |
Number | 218 |
Pagination | 26 |
Date Published | 04/2023 |
Publisher | MDPI |
Abstract | 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 Key | 43214 |