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 51–60 of 227 papers

TitleStatusHype
Quality assurance of organs-at-risk delineation in radiotherapy—0
One-Class Classification as GLRT for Jamming Detection in Private 5G Networks—0
Critical Review for One-class Classification: recent advances and the reality behind them—0
Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing—0
LLM meets Vision-Language Models for Zero-Shot One-Class Classification—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification—0
Trustworthiness of X Users: A One-Class Classification Approach—0
Understanding Time Series Anomaly State Detection through One-Class Classification—0
Show:102550
← PrevPage 6 of 23Next →

No leaderboard results yet.