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Label-Informed Outlier Detection Based on Granule Density

arXiv 2512.18774 ↗First posted 21 Dec 2025cs.LGcited by 0

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Abstract (arXiv)

Outlier detection, crucial for identifying unusual patterns with significant implications across numerous applications, has drawn considerable research interest. Existing semi-supervised methods typically treat data as purely numerical and} in a deterministic manner, thereby neglecting the heterogeneity and uncertainty inherent in complex, real-world datasets. This paper introduces a label-informed outlier detection method for heterogeneous data based on Granular Computing and Fuzzy Sets, namely Granule Density-based Outlier Factor (GDOF). Specifically, GDOF first employs label-informed fuzzy granulation to effectively represent various data types and develops granule density for precise density estimation. Subsequently, granule densities from individual attributes are integrated for outlier scoring by assessing attribute relevance with a limited number of labeled outliers. Experimental results on various real-world datasets show that GDOF stands out in detecting outliers in heterogeneous data with a minimal number of labeled outliers. The integration of Fuzzy Sets and Granular Computing in GDOF offers a practical framework for outlier detection in complex and diverse data types. All relevant datasets and source codes are publicly available for further research. This is the author's accepted manuscript of a paper published in IEEE Transactions on Fuzzy Systems. The final version is available at https://doi.org/10.1109/TFUZZ.2024.3514853

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References · 18

  1. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  2. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  3. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  4. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  5. 2019Deep Anomaly Detection with Deviation Networkscited by 152
  6. 2020COPOD: Copula-Based Outlier Detectioncited by 143
  7. 2022Deep Isolation Forest for Anomaly Detectioncited by 90
  8. 2018Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detectioncited by 79
  9. 2014Toward Supervised Anomaly Detectioncited by 73
  10. 2019Deep Weakly-supervised Anomaly Detectioncited by 59
  11. 2019XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learningcited by 42
  12. 2020Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Datacited by 42
6 more
  1. 2021Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detectioncited by 42
  2. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
  3. 2020Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learningcited by 34
  4. 2023Weakly Supervised Anomaly Detection: A Surveycited by 22
  5. 2022Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANscited by 11
  6. 2020ESAD: End-to-end Deep Semi-supervised Anomaly Detectioncited by 8

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