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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
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays—0
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
Deep Visual Anomaly detection with Negative Learning—0
A Unifying Review of Deep and Shallow Anomaly Detection—0
Deep One-Class Classification Using Intra-Class Splitting—0
Applying support vector data description for fraud detection—0
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma—0
Deep Inverse Reinforcement Learning via Adversarial One-Class Classification—0
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