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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 51–75 of 227 papers

TitleStatusHype
NFAD: Fixing anomaly detection using normalizing flowsCode0
Active Authentication using an Autoencoder regularized CNN-based One-Class ClassifierCode0
Metre as a stylometric feature in Latin hexameter poetryCode0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural NetworksCode0
Disentangling Tabular Data Towards Better One-Class Anomaly DetectionCode0
One-Class Classification by Ensembles of Regression models -- a detailed studyCode0
Linear-time One-Class Classification with Repeated Element-wise FoldingCode0
LBL: Logarithmic Barrier Loss Function for One-class ClassificationCode0
Learning Deep Features for One-Class ClassificationCode0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
Impact of Channel Variation on One-Class Learning for Spoof DetectionCode0
Generalized Reference Kernel for One-class ClassificationCode0
Graph-Embedded Subspace Support Vector Data DescriptionCode0
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled DataCode0
Ellipsoidal Subspace Support Vector Data DescriptionCode0
CA2: Class-Agnostic Adaptive Feature Adaptation for One-class ClassificationCode0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring SignalsCode0
MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with Spatial TranscriptomicsCode0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
DROCC: Deep Robust One-Class Classification—0
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification—0
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