一种可处理多种数据类型的异常检测新方法，无需转换格式即可提升效果。
Random Similarity Isolation Forests
- 基于相似性投影与隔离思想，统一处理数值、时间序列、图像等多模态数据。
- 在47个基准数据集上优于5种主流方法，平均性能显著提升。
- 适合需要融合复杂数据类型的工业场景，如金融风控、设备监控。
随着预测模型广泛应用，企业收集的数据类型日益丰富，不仅包含简单数值特征，还涵盖时间序列、图像、图结构等复杂对象。这些多模态数据有望提升异常检测等任务的性能，但现有算法仅针对单一数据类型设计。处理混合数据时，需融合多个专用模型或统一转换表示，易影响效果。本文提出一种名为随机相似性隔离森林（Random Similarity Isolation Forest）的多模态异常检测算法，结合隔离机制与基于相似性的投影策略，可直接处理任意类型数据的混合。在47个基准数据集上的实验表明，该方法超越五种前沿竞争者。研究证实多模态信息能有效提升异常检测能力，并强调需为多模态算法建立专门的评估基准。
原文摘要 · Abstract (English)
With predictive models becoming prevalent, companies are expanding the types of data they gather. As a result, the collected datasets consist not only of simple numerical features but also more complex objects such as time series, images, or graphs. Such multi-modal data have the potential to improve performance in predictive tasks like outlier detection, where the goal is to identify objects deviating from the main data distribution. However, current outlier detection algorithms are dedicated to individual types of data. Consequently, working with mixed types of data requires either fusing multiple data-specific models or transforming all of the representations into a single format, both of which can hinder predictive performance. In this paper, we propose a multi-modal outlier detection algorithm called Random Similarity Isolation Forest. Our method combines the notions of isolation and similarity-based projection to handle datasets with mixtures of features of arbitrary data types. Experiments performed on 47 benchmark datasets demonstrate that Random Similarity Isolation Forest outperforms five state-of-the-art competitors. Our study shows that the use of multiple modalities can indeed improve the detection of anomalies and highlights the need for new outlier detection benchmarks tailored for multi-modal algorithms.
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