outlier ensembles

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (7 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PyOD: A Python Toolbox for Scalable Outlier Detection
  6 Jan 2019 4 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.
• A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
  18 Nov 2020 1 repository listed
  The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care.
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
  11 Mar 2020 1 repository listed
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
• DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles
  23 Nov 2019 1 repository listed
  Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth.
• LSCP: Locally Selective Combination in Parallel Outlier Ensembles
  4 Dec 2018 1 repository listed
  The top-performing base detectors in this local region are selected and combined as the model's final output.
• Graph-based Selective Outlier Ensembles
  17 Apr 2018 1 repository listed
  A problem with this approach is that poor components are likely to negatively affect the quality of the consensus result.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.