arXiv：cs.AI· Nassim Belmecheri, Noureddine Aribi, Nadjib Lazaar, Yahia Lebbah, Samir Loudni·· 4 小时前
用 AHP 与主动学习提升模式排序函数的学习效率
Boosting the Learning for Ranking Patterns
AI 导读
该研究将模式排序函数的学习建模为多准则决策问题，用层次分析法（AHP）根据用户偏好为不同兴趣度度量引出权重。作者还提出带敏感性启发式的主动学习模式，在减少用户排序查询次数的同时保持高质量结果。实验显示，相比当前最先进方法，该方案显著缩短运行时间，返回精确的模式排序，并对用户误差具有鲁棒性。
正文
Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.16511 [cs.AI] |
| (or arXiv:2610.16511v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16511 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.3390/a16050218
DOI(s) linking to related resources |
Submission history
From: Nassim Belmecheri [view email]
[v1]
Mon, 12 Oct 2026 10:00:58 UTC (191 KB)
来源：arXiv：cs.AI · arxiv.org