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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 191–200 of 227 papers

TitleStatusHype
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
One-Class Adversarial Nets for Fraud DetectionCode0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Subspace Support Vector Data DescriptionCode0
Deep One-Class ClassificationCode0
Linear-time One-Class Classification with Repeated Element-wise FoldingCode0
UNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTIONCode0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural NetworksCode0
Active Authentication using an Autoencoder regularized CNN-based One-Class ClassifierCode0
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