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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 91–100 of 227 papers

TitleStatusHype
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies—0
Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder—0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural NetworksCode0
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation—0
Query by example in remote sensing image archive using enhanced deep support vector data descriptionCode0
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection—0
An Upper Bound for the Distribution Overlap Index and Its Applications—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection—0
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