RiForest通过软稀疏投影和谷值强化提升异常检测稳定性和鲁棒性。
Robust Isolation Forest using Soft Sparse Random Projection and Valley Emphasis Method
- 用软稀疏随机投影生成超平面，自动选优分割特征。
- 在24个基准数据集上均优于现有方法，对噪声变量鲁棒。
- 适合处理稀疏、分布广的罕见异常，稳定性强。
Isolation Forest（iForest）是一种基于异常“稀少且不同”假设的无监督异常检测算法。尽管已有多种改进方法，但这些算法在不同数据集上的表现差异显著，且难以有效分离稀有且分布广泛异常。为此，本文提出稳健型iForest（RiForest）。RiForest结合现有特征与通过软稀疏随机投影获得的随机超平面，自动识别适用于各类数据集的优质分割特征。同时引入被低估的谷值强化方法，实现最优分割点选择，并在软稀疏随机投影中加入稀疏性随机化，进一步提升异常检测鲁棒性。在24个基准数据集上的实验表明，RiForest在异常检测性能上持续领先于现有算法，展现出优异的稳定性和对噪声变量的鲁棒性。
原文摘要 · Abstract (English)
Isolation Forest (iForest) is an unsupervised anomaly detection algorithm designed to effectively detect anomalies under the assumption that anomalies are ``few and different." Various studies have aimed to enhance iForest, but the resulting algorithms often exhibited significant performance disparities across datasets. Additionally, the challenge of isolating rare and widely distributed anomalies persisted in research focused on improving splits. To address these challenges, we introduce Robust iForest (RiForest). RiForest leverages both existing features and random hyperplanes obtained through soft sparse random projection to identify superior split features for anomaly detection, independent of datasets. It utilizes the underutilized valley emphasis method for optimal split point determination and incorporates sparsity randomization in soft sparse random projection for enhanced anomaly detection robustness. Across 24 benchmark datasets, experiments demonstrate RiForest's consistent outperformance of existing algorithms in anomaly detection, emphasizing stability and robustness to noise variables.
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