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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 151–160 of 227 papers

TitleStatusHype
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems—0
_p-Norm Multiple Kernel One-Class Fisher Null-Space—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Quantum One-class Classification With a Distance-based ClassifierCode0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
G2D: Generate to Detect Anomaly—0
Use of in-the-wild images for anomaly detection in face anti-spoofing—0
Applying support vector data description for fraud detection—0
Policy Entropy for Out-of-Distribution Classification—0
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