Boundary Peeling: Outlier Detection Method Using One-Class Peeling
Sheikh Arafat Na Sun Maria L. Weese Waldyn G. Martinez
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.