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Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets

arXiv 2512.18977 ↗First posted 22 Dec 2025cs.LGcited by 0

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Abstract (arXiv)

Outlier detection aims to find samples that behave differently from the majority of the data. Semi-supervised detection methods can utilize the supervision of partial labels, thus reducing false positive rates. However, most of the current semi-supervised methods focus on numerical data and neglect the heterogeneity of data information. In this paper, we propose a consistency-guided outlier detection algorithm (COD) for heterogeneous data with the fuzzy rough set theory in a semi-supervised manner. First, a few labeled outliers are leveraged to construct label-informed fuzzy similarity relations. Next, the consistency of the fuzzy decision system is introduced to evaluate attributes' contributions to knowledge classification. Subsequently, we define the outlier factor based on the fuzzy similarity class and predict outliers by integrating the classification consistency and the outlier factor. The proposed algorithm is extensively evaluated on 15 freshly proposed datasets. Experimental results demonstrate that COD is better than or comparable with the leading outlier detectors. This manuscript is the accepted author version of a paper published by Elsevier. The final published version is available at https://doi.org/10.1016/j.asoc.2024.112070

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References · 17

  1. 2016Adversarial Feature Learningcited by 350
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  6. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
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  12. 2020Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Datacited by 42
5 more
  1. 2021Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detectioncited by 42
  2. 2020Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learningcited by 34
  3. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  4. 2022Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANscited by 11
  5. 2020ESAD: End-to-end Deep Semi-supervised Anomaly Detectioncited by 8

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