@article {43214,
	title = {Boosting the Learning for Ranking Patterns},
	journal = {Algorithms},
	volume = {16},
	number = {218},
	year = {2023},
	month = {04/2023},
	pages = {26},
	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.},
	author = {Belmecheri, Nassim and Noureddine, Aribi and Lazaar, Nadjib and Lebbah, Yahia and Loudni, Samir}
}
