发表机构
Budapest University of Technology and Economics（布达佩斯技术与经济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出RIFT异常检测方法，其一维数据得分可精确还原孤立森林极限，高维数据无参数且鲁棒，集成变体在ADBench等数据集上准确率与IF相当、方差更低。
AI 中文摘要
孤立森林（Isolation Forest，IF）是一种广泛应用的无监督异常检测基线方法。近期研究给出了一维数据下无限森林极限的闭式表达式。受该公式几何解释的启发，我们提出RIFT（Relative Isolation From Trees，树相对隔离），这是一种确定性异常检测方法，它生成最小生成树，并根据每个点视角下树边的表观大小之和对该点打分。对于一维数据，RIFT得分可精确还原IF的闭式极限；在高维数据中，它提供了一种无参数的泛化方法，具有确定性、对密度变化和聚类异常值鲁棒的特点，且避免了IF的轴平行伪影。我们还针对大数据集提出了集成变体。在合成数据和ADBench基准上的实验表明，其准确率与IF相当，而集成变体在不同随机种子间的方差显著更低。
英文摘要
Isolation Forest (IF) is a widely used baseline for unsupervised anomaly detection. Recent studies provide a closed-form expression for the infinite-forest limit for one-dimensional data. Inspired by the geometric interpretation of this formula, we introduce RIFT (Relative Isolation From Trees), a deterministic anomaly detection method that generates the minimum spanning tree and scores each point by the sum of the apparent sizes of tree edges as viewed from that point. For one-dimensional data, the RIFT score recovers the closed-form IF limit exactly. In higher dimensions, it provides a parameter-free generalization that is deterministic, robust to varying density and clustered anomalies and avoids the axis-parallel artifacts of IF. We further propose an ensemble variant for large datasets. Experiments on synthetic data and the ADBench benchmark demonstrate that the accuracy is comparable to IF, while the ensemble variant exhibits significantly lower variance across random seeds.
