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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 126–150 of 227 papers

TitleStatusHype
Federated Learning-based Active Authentication on Mobile Devices—0
One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Optimised one-class classification performance—0
Average Localised Proximity: A new data descriptor with good default one-class classification performance—0
Deep One-Class Classification via Interpolated Gaussian DescriptorCode1
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition—0
One-Class Classification: A Survey—0
One-class Classification Robust to Geometric Transformation—0
Towards Fair Deep Anomaly Detection—0
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly DetectionCode1
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network—0
FROCC: Fast Random projection-based One-Class Classification—0
Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification—0
Learning and Evaluating Representations for Deep One-class ClassificationCode1
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm—0
A Unifying Review of Deep and Shallow Anomaly Detection—0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems—0
Meta Learning for Few-Shot One-class ClassificationCode1
_p-Norm Multiple Kernel One-Class Fisher Null-Space—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Quantum One-class Classification With a Distance-based ClassifierCode0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
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