# Geometry-Aware Active Learning of Pattern Rankings via Choquet-Based Aggregation

arXiv: https://arxiv.org/abs/2507.14217 · HTML: https://mlsanity.com/paper/2507.14217/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 16 Jul 2025.

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

- [Learning what matters - Sampling interesting patterns](https://mlsanity.com/paper/1702.01975/) (2017)
- [Generalized Binary Search For Split-Neighborly Problems](https://mlsanity.com/paper/1802.09751/) (2018)

## Abstract (arXiv)

We address the pattern explosion problem in pattern mining by proposing an interactive learning framework that combines nonlinear utility aggregation with geometry-aware query selection. Our method models user preferences through a Choquet integral over multiple interestingness measures and exploits the geometric structure of the version space to guide the selection of informative comparisons. A branch-and-bound strategy with tight distance bounds enables efficient identification of queries near the decision boundary. Experiments on UCI datasets show that our approach outperforms existing methods such as ChoquetRank, achieving better ranking accuracy with fewer user interactions.