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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
Generative Cooperative Learning for Unsupervised Video Anomaly Detection—0
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)—0
Critical Review for One-class Classification: recent advances and the reality behind them—0
A Perceptron-based Fine Approximation Technique for Linear Separation—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification—0
Entropic one-class classifiers—0
Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing—0
Deep Inverse Reinforcement Learning via Adversarial One-Class Classification—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
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