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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 51–60 of 227 papers

TitleStatusHype
Applying support vector data description for fraud detection—0
Deep One-Class Classification Using Intra-Class Splitting—0
Deep Visual Anomaly detection with Negative Learning—0
Critical Review for One-class Classification: recent advances and the reality behind them—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders—0
Average Localised Proximity: A new data descriptor with good default one-class classification performance—0
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification—0
DOC3-Deep One Class Classification using Contradictions—0
A Perceptron-based Fine Approximation Technique for Linear Separation—0
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