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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 101–110 of 227 papers

TitleStatusHype
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication—0
A One-Class Classification method based on Expanded Non-Convex HullsCode0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
[Reproducibility Report] Explainable Deep One-Class Classification—0
Generalized Reference Kernel for One-class ClassificationCode0
OCFormer: One-Class Transformer Network for Image Classification—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography—0
Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis—0
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