Consensus across univariate z-scores, Mahalanobis distance, Isolation Forest, Local Outlier Factor, and One-Class SVM identifies consistent multivariate outliers among European regions that reflect real structural differences rather than data errors.
Identifying resilient and vulnerable regionsthroughunsupervisedanomalydetection.Papers in Regional Science, 102(1):89–113
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Unsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics
Consensus across univariate z-scores, Mahalanobis distance, Isolation Forest, Local Outlier Factor, and One-Class SVM identifies consistent multivariate outliers among European regions that reflect real structural differences rather than data errors.