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One-Class Classification

One-class classification (OCC) algorithms serve a crucial role in scenarios where the negative class is either absent, poorly sampled, or not well defined. This unique situation presents a challenge for building effective classifiers, as they must delineate the class boundary solely based on knowledge of the positive class. OCC has found application in various research domains, including outlier/novelty detection and concept learning.

In the context of anomaly detection, OCC models are trained exclusively on "normal" data and are subsequently tasked with identifying anomalous patterns during inference.

A one-class classifier aims at capturing characteristics of training instances, in order to be able to distinguish between them and potential outliers to appear.

— Page 139, Learning from Imbalanced Data Sets, 2018.

Papers

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Showing 11–20 of 227 papers

TitleStatusHype
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly DetectionCode1
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class ClassificationCode1
Learning and Evaluating Representations for Deep One-class ClassificationCode1
Learning Not to Reconstruct AnomaliesCode1
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Anomaly Detection in Emails using Machine Learning and Header InformationCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel DescriptorsCode1
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly DetectionCode1
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