用轮廓系数增强孤立森林，提升无监督交易欺诈检测效果
SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection
- 在树结构生成的表示空间中计算轮廓得分，量化样本与聚类组的匹配度
- 在IEEE-CIS数据集上相比原孤立森林平均提升0.0080 AUC-PR，五次实验全胜
- 提供可调参数，明确指出在真实数据有效但在合成数据无效的边界条件
无监督异常检测广泛应用于标签稀缺的交易欺诈检测。孤立森林（IF）因可扩展性和部署简便而成为主流经典方法。本文提出SilIF，一种对孤立森林的增强方法，其在森林生成的表示空间中加入基于轮廓系数的评分层。对每个样本，提取各树路径长度构成的“指纹”向量，聚类成结构组，并计算轮廓分数以衡量其归属组的契合度与最近邻组的差异。该轮廓信号通过单一超参数α与原始IF分数融合。在包含约59万笔交易、欺诈占比3.5%的IEEE-CIS欺诈检测基准上，当α=1.0时，SilIF相比普通孤立森林平均提升0.0080 AUC-PR，且在五次种子实验中全部胜出（配对t检验p=0.046）。同时在合成信用卡数据集Sparkov上未见提升，并分析了导致差异的条件。论文将SilIF呈现为可调、易部署的增强方案，坦诚说明其适用边界。代码已开源。
原文摘要 · Abstract (English)
Unsupervised anomaly detection is widely used in transaction fraud detection where labels are scarce. Isolation Forest (IF) is among the most popular classical methods due to its scalability and ease of deployment. We propose SilIF, an augmentation of Isolation Forest that adds a silhouette-based scoring layer computed in a representation space induced by the trees of the forest. For each point, we extract a vector of per-tree path lengths, cluster these "fingerprints" into structural groups, and compute a silhouette score that measures how well the point fits its assigned group versus the nearest alternative. The silhouette signal is combined with the base IF score via a single hyperparameter alpha. On the IEEE-CIS Fraud Detection benchmark (~590K transactions, 3.5% fraud), SilIF with alpha=1.0 improves over plain Isolation Forest by +0.0080 AUC-PR on average across five seeds, with SilIF winning on all five seeds (paired t-test p=0.046). We also report results on a synthetic credit-card dataset (Sparkov) where the silhouette augmentation does not improve over plain IF, and we characterize the conditions that distinguish the two outcomes. The paper presents SilIF as a tunable, easy-to-deploy enhancement to Isolation Forest with honest reporting of when it helps and when it does not. Code at https://github.com/venkat15vk/silif-anomaly-detection.
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