Unsupervised anomaly detection is a challenging problem due to the diversity of data distributions and the lack of labels. Ensemble methods are often adopted to mitigate these challenges by combining multiple detectors, which can reduce individual biases and increase robustness. Yet building an ensemble that is genuinely complementary remains challenging, since many detectors rely on similar decision cues and end up producing redundant anomaly scores. As a result, the potential of ensemble learning is often limited by the difficulty of identifying models that truly capture different types of irregularities. To address this, we propose a methodology for characterizing anomaly detectors through their decision mechanisms. Using SHapley Additive exPlanations, we quantify how each model attributes importance to input features, and we use these attribution profiles to measure similarity between detectors. We show that detectors with similar explanations tend to produce correlated anomaly scores and identify largely overlapping anomalies. Conversely, explanation divergence reliably indicates complementary detection behavior. Our results demonstrate that explanation-driven metrics offer a different criterion than raw outputs for selecting models in an ensemble. However, we also demonstrate that diversity alone is insufficient; high individual model performance remains a prerequisite for effective ensembles. By explicitly targeting explanation diversity while maintaining model quality, we are able to construct ensembles that are more diverse, more complementary, and ultimately more effective for unsupervised anomaly detection.

翻译：无监督异常检测因数据分布的多样性和缺乏标签而面临挑战。集成方法通常通过组合多个检测器来缓解这些问题，从而减少个体偏差并增强鲁棒性。然而，构建一个真正互补的集成仍然困难重重，因为许多检测器依赖相似的决策线索，最终产生冗余的异常分数。因此，集成学习的潜力常受限于难以识别真正捕捉不同类型异常的模型。为解决这一问题，我们提出了一种通过决策机制来刻画异常检测器特征的方法。利用沙普利可加解释，我们量化了每个模型对输入特征的重要性分配，并利用这些归因分布来度量检测器之间的相似性。我们表明，具有相似解释的检测器倾向于产生相关的异常分数，并识别出大量重叠的异常。相反，解释的差异性则可靠地指示互补的检测行为。我们的结果证明，基于解释的指标为集成中的模型选择提供了不同于原始输出的标准。然而，我们也证明仅凭多样性不足以保证效果；个体模型的高性能仍是构建有效集成的先决条件。通过明确追求解释多样性同时保持模型质量，我们能够构建出更富多样性、更具互补性，并最终在无监督异常检测中更有效的集成模型。