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 101–125 of 227 papers

TitleStatusHype
One-class Classification Robust to Geometric Transformation—0
One-Class Classification: Taxonomy of Study and Review of Techniques—0
One-Class Feature Learning Using Intra-Class Splitting—0
One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification—0
One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
Online Learning with Regularized Kernel for One-class Classification—0
On the Adversarial Robustness of Benjamini Hochberg—0
On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox—0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
Open-Set Language Identification—0
Optimised one-class classification performance—0
Comparison of Statistical and Machine Learning Techniques for Physical Layer Authentication—0
Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor—0
Policy Entropy for Out-of-Distribution Classification—0
Probabilistic Saliency Estimation—0
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation—0
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies—0
Quality assurance of organs-at-risk delineation in radiotherapy—0
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification—0
Relationship between Variants of One-Class Nearest Neighbours and Creating their Accurate Ensembles—0
[Reproducibility Report] Explainable Deep One-Class Classification—0
Restricted Generative Projection for One-Class Classification and Anomaly Detection—0
Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis—0
Robust One-Class Kernel Spectral Regression—0
Show:102550
← PrevPage 5 of 10Next →

No leaderboard results yet.