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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 61–70 of 227 papers

TitleStatusHype
LBL: Logarithmic Barrier Loss Function for One-class ClassificationCode0
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on TextCode0
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled DataCode0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Generalized Reference Kernel for One-class ClassificationCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Ellipsoidal Subspace Support Vector Data DescriptionCode0
CA2: Class-Agnostic Adaptive Feature Adaptation for One-class ClassificationCode0
Graph-Embedded Subspace Support Vector Data DescriptionCode0
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring SignalsCode0
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