Granular-Ball Subspace-Based Fuzzy Neighborhood Anomaly Detector

Unsupervised anomaly detection has attracted considerable attention in complex data environments due to its independence from costly labeled data. Among various approaches, subspace sampling-based ensemble methods, such as IForest, have been widely adopted for their simplicity and computational efficiency. However, these methods typically operate under single granularity, which limits the diversity of subspaces and hinders the ability to capture hierarchical structures and complex patterns in the data. Moreover, they often overlook uncertainty information such as fuzziness among samples, which constrains their capacity to model complex relationships. To address these limitations, this paper proposes a method called Granular-Ball Subspace-based Fuzzy Neighborhood Anomaly Detector (GSFAD). The proposed method integrates granular-ball subspace ensemble learning with a fuzzy computing framework, achieving a balance between computational efficiency and the ability to model multi-granularity fuzzy structures. Specifically, the algorithm begins by performing multi-granularity aggregation with granular-balls to cover the origin data. Then, regions potentially containing anomalies are filtered out based on granular-ball characteristics before sampling. Building on this, multiple granularball subspaces are constructed via repeated sampling, and fuzzy relations between granular-balls are computed within each subspace. Finally, the anomaly score of each sample is assessed by fusing the fuzzy neighborhood information across all subspaces. Experimental results on 20 benchmark datasets demonstrate that GSFAD consistently outperforms existing subspace sampling methods that operate under a single granularity. In addition, it achieves superior performance compared to 15 state-of-theart anomaly detection techniques. The code is publicly available online at https://github.com/Caspar-lab/GSFAD.