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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 31–40 of 227 papers

TitleStatusHype
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing—0
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images—0
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
Average Localised Proximity: A new data descriptor with good default one-class classification performance—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays—0
A Unifying Review of Deep and Shallow Anomaly Detection—0
Applying support vector data description for fraud detection—0
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