Boundary peeling: An outlier detection method

Unsupervised outlier detection constitutes a crucial phase within data analysis and remains an open area 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 Boundary Peeling, an unsupervised outlier detection algorithm. Boundary Peeling uses the average signed distance from iteratively peeled, flexible boundaries generated by one-class support vector machines to flag outliers. The method is similar to convex hull peeling but well suited for high-dimensional data and has flexibility to adapt to different distributions. Boundary Peeling has robust hyperparameter settings and, for increased flexibility, can be cast as an ensemble method. In unimodal and multimodal synthetic data simulations 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. Boundary Peeling performs competitively or better in terms of correct classification, AUC, and processing time using semantically meaningful benchmark datasets.