Unsupervised outlier detection constitutes a crucial phase within data analysis and remains a dynamic realm of research. A good outlier detection algorithm should be computationally efficient, robust to tuning parameter selection, and perform consistently well across diverse underlying data distributions. We introduce One-Class Boundary Peeling, an unsupervised outlier detection algorithm. One-class Boundary Peeling uses the average signed distance from iteratively-peeled, flexible boundaries generated by one-class support vector machines. One-class Boundary Peeling has robust hyperparameter settings and, for increased flexibility, can be cast as an ensemble method. In synthetic data simulations One-Class Boundary Peeling outperforms all state of the art methods when no outliers are present while maintaining comparable or superior performance in the presence of outliers, as compared to benchmark methods. One-Class Boundary Peeling performs competitively in terms of correct classification, AUC, and processing time using common benchmark data sets.

非监督异常检测的一个重要阶段是数据分析中的一个关键领域，我们引入了一种名为一类边界剥离的非监督异常检测算法，它使用了由一类支持向量机生成的灵活边界的迭代剥离的平均有符号距离，并具有强大的超参数设置和可以被视为合奏方法的灵活性，通过在合成数据模拟中超过所有先进方法，保持与基准方法相当或更好的性能，无论是否存在异常值，以及在使用常见基准数据集时在正确分类、AUC和处理时间方面具有竞争力。

边界剥离：一种使用单类剥离的异常检测方法