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New method explains outliers in interval-valued data using Shapley values

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for explaining outliers in interval-valued data, addressing a gap in current outlier detection techniques. This approach utilizes Shapley values to provide a detailed breakdown of why an observation is considered an outlier, attributing contributions to specific variables and their interactions. The method offers a fine-grained interpretation, identifying variable-specific outliers that might otherwise be missed. AI

IMPACT Provides a more interpretable approach to outlier detection in complex datasets, potentially improving the reliability of AI models that rely on clean data.

RANK_REASON The cluster contains an academic paper detailing a new statistical methodology.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method explains outliers in interval-valued data using Shapley values

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Catarina P. Loureiro, M. Ros\'ario Oliveira, Paula Brito, Lina Oliveira ·

    Explainable Outlier Detection for Interval-valued Data

    arXiv:2606.26307v1 Announce Type: cross Abstract: Explainability is increasingly recognized as a key aspect of outlier detection. However, for complex data structures such as interval-valued data, it remains largely unexplored. Building on an outlier detection framework based on …

  2. arXiv stat.ML TIER_1 English(EN) · Lina Oliveira ·

    Explainable Outlier Detection for Interval-valued Data

    Explainability is increasingly recognized as a key aspect of outlier detection. However, for complex data structures such as interval-valued data, it remains largely unexplored. Building on an outlier detection framework based on the Interval Minimum Covariance Determinant estima…

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