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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 71–80 of 227 papers

TitleStatusHype
Active anomaly detection based on deep one-class classification—0
From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints—0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection—0
Conceptron: a probabilistic deep one-class classification method—0
Combining One-Class Classifiers via Meta-Learning—0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach—0
Federated Learning-based Active Authentication on Mobile Devices—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
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