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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 111–120 of 227 papers

TitleStatusHype
Open-Set Language Identification—0
Optimised one-class classification performance—0
Comparison of Statistical and Machine Learning Techniques for Physical Layer Authentication—0
Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor—0
Policy Entropy for Out-of-Distribution Classification—0
Probabilistic Saliency Estimation—0
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation—0
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies—0
Quality assurance of organs-at-risk delineation in radiotherapy—0
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification—0
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