AI RESEARCH
DCFO: Density-Based Counterfactuals for Outliers -- Additional Material
arXiv CS.LG
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ArXi:2512.10659v3 Announce Type: replace Outlier detection identifies data points that significantly deviate from the majority of the data distribution. Explaining outliers is crucial for understanding the underlying factors that contribute to their detection, validating their significance, and identifying potential biases or errors. Effective explanations provide actionable insights, facilitating preventive measures to avoid similar outliers in the future.