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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 151–200 of 227 papers

TitleStatusHype
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
LLM meets Vision-Language Models for Zero-Shot One-Class Classification—0
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection—0
Lp-Norm Constrained One-Class Classifier Combination—0
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images—0
Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification—0
Mobile authentication of copy detection patterns—0
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification—0
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
One-Class Classification: A Survey—0
One-Class Classification for Intrusion Detection on Vehicular Networks—0
One-Class Classification for Wafer Map using Adversarial Autoencoder with DSVDD Prior—0
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
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled DataCode0
LBL: Logarithmic Barrier Loss Function for One-class ClassificationCode0
Impact of Channel Variation on One-Class Learning for Spoof DetectionCode0
Learning Deep Features for One-Class ClassificationCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
One-Class Adversarial Nets for Fraud DetectionCode0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Subspace Support Vector Data DescriptionCode0
Deep One-Class ClassificationCode0
Linear-time One-Class Classification with Repeated Element-wise FoldingCode0
UNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTIONCode0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural NetworksCode0
Active Authentication using an Autoencoder regularized CNN-based One-Class ClassifierCode0
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