# Boosting the Learning for Ranking Patterns

arXiv: https://arxiv.org/abs/2610.16511 · HTML: https://mlsanity.com/paper/2610.16511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 2 of 2)

- [On interestingness measures of formal concepts](https://mlsanity.com/paper/1611.02646/) (2016)
- [Learning what matters - Sampling interesting patterns](https://mlsanity.com/paper/1702.01975/) (2017)

## Abstract (arXiv)

Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness mea- sures 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 multicriteria decision making problem. Our approach uses 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 error, compared to state-of-the-art approaches.