Lorenzo-Perini/Active_PU_Learning

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Created over 4 years ago
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Lorenzo-Perini/Active_PU_Learning

Lorenzo-Perini

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Class Prior Estimation in Active Positive and Unlabeled Learning

Confidence_AD

Estimation of the confidence measure for anomaly detectors, as explained in the paper "Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions" (ECML-PKDD 2020).

Jupyter Notebook

TransferContamination

Code and supplementary material of the paper "Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity", AAAI22.

Jupyter Notebook

RejEx

Jupyter Notebook

StabilityRankings_AD

A Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods

Jupyter Notebook

GammaGMM

Implementation of the methodology proposed by the paper "Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection".

Jupyter Notebook

PU-MIL-AD

Jupyter Notebook

Ballad

Implementation of the method BALLAD, published at the workshop UDM23 @ AAAI.

Jupyter Notebook