用球形半径表示密度，高效识别异常点。
Isolation-based Spherical Ensemble Representations for Anomaly Detection
- 以超球半径替代密度估计，实现线性时间、常数空间计算
- 在22个真实数据集上优于11种基线方法，尤其擅长检测局部异常
- 适合需要高效、高精度异常检测的工业场景
异常检测在数据挖掘与管理中至关重要，广泛应用于欺诈检测、网络安全和日志监控。尽管研究众多，现有无监督方法仍面临分布假设冲突、计算效率低及难以处理多种异常类型等挑战。为此，我们提出ISER（基于隔离的球形集成表示），通过将超球半径作为局部密度的代理，扩展了传统基于隔离的方法，同时保持线性时间与常数空间复杂度。ISER构建集成表示，其中较小的球半径代表密集区域，较大的半径对应稀疏区域。引入一种新颖的基于相似性的评分方法，通过比较集成表示与理论异常参考模式来衡量模式一致性。此外，我们结合ISER改进Isolation Forest，调整评分函数以缓解轴对齐偏差和局部异常检测局限。在22个真实数据集上的全面实验表明，ISER在性能上显著优于11种基线方法。
原文摘要 · Abstract (English)
Anomaly detection is a critical task in data mining and management with applications spanning fraud detection, network security, and log monitoring. Despite extensive research, existing unsupervised anomaly detection methods still face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types. To address these problems, we propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as proxies for local density characteristics while maintaining linear time and constant space complexity. ISER constructs ensemble representations where hypersphere radii encode density information: smaller radii indicate dense regions while larger radii correspond to sparse areas. We introduce a novel similarity-based scoring method that measures pattern consistency by comparing ensemble representations against a theoretical anomaly reference pattern. Additionally, we enhance the performance of Isolation Forest by using ISER and adapting the scoring function to address axis-parallel bias and local anomaly detection limitations. Comprehensive experiments on 22 real-world datasets demonstrate ISER's superior performance over 11 baseline methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。