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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 201–210 of 227 papers

TitleStatusHype
A One-Class Classification Decision Tree Based on Kernel Density Estimation—0
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
Improve Uncertainty Estimation for Unknown Classes in Bayesian Neural Networks with Semi-Supervised /One Set Classification—0
One-Class Adversarial Nets for Fraud DetectionCode0
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
Subspace Support Vector Data DescriptionCode0
Learning Deep Features for One-Class ClassificationCode0
Semi-supervised Outlier Detection using Generative And Adversary Framework—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising—0
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