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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 81–90 of 227 papers

TitleStatusHype
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints—0
Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
G2D: Generate to Detect Anomaly—0
Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication—0
Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
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