用几何方法选关键对比，减少用户交互次数提升排序精度
Geometry-Aware Active Learning of Pattern Rankings via Choquet-Based Aggregation
- 用Choquet积分融合多个有趣性指标建模用户偏好
- 通过版本空间几何结构定位高信息量的查询点
- 在UCI数据集上以更少交互达到更高排名准确率
我们针对模式挖掘中的模式爆炸问题，提出一种交互式学习框架，结合非线性效用聚合与几何感知查询选择。该方法通过在多个有趣性度量上使用Choquet积分建模用户偏好，并利用版本空间的几何结构指导信息量高的比较选择。采用带有紧距离边界的分支定界策略，高效识别决策边界附近的查询。在UCI数据集上的实验表明，本方法优于现有方法如ChoquetRank，以更少的用户交互实现更高的排名准确率。
原文摘要 · Abstract (English)
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.
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