SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 161–170 of 227 papers

TitleStatusHype
Model Selection for Anomaly Detection—0
Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis—0
Multi-layer Kernel Ridge Regression for One-class Classification—0
Multi-Task Kernel Null-Space for One-Class Classification—0
Newton Method-based Subspace Support Vector Data Description—0
Non-Robust Features are Not Always Useful in One-Class Classification—0
OCFormer: One-Class Transformer Network for Image Classification—0
OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns—0
One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring—0
One-Class Classification as GLRT for Jamming Detection in Private 5G Networks—0
Show:102550
← PrevPage 17 of 23Next →

No leaderboard results yet.