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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 161–170 of 227 papers

TitleStatusHype
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition—0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays—0
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing—0
An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm—0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination—0
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach—0
Anomaly detection with semi-supervised classification based on risk estimators—0
An Upper Bound for the Distribution Overlap Index and Its Applications—0
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset—0
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