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Found 15 papers, 5 papers with code
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage.
Trustworthiness of $\mathbb{X}$ Users: A One-Class Classification Approach
In this study, we use various OCC models for $\mathbb{X}$ user classification.
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification
The study highlights the potential of subspace learning-based OCC algorithms by investigating the limitations of current fraud detection strategies and the specific challenges of credit card fraud detection.
Convolutional autoencoder-based multimodal one-class classification
We show that such regularizers improve performance.
One-Class Classification for Intrusion Detection on Vehicular Networks
Research has been done on the efficacy of these methods, most notably One-Class Support Vector Machine and Support Vector Data Description, but many new extensions of these works have been proposed and have yet to be tested for injection attacks in vehicular networks.
Newton Method-based Subspace Support Vector Data Description
The objective of S-SVDD is to map the original data to a subspace optimized for one-class classification, and the iterative optimization process of data mapping and description in S-SVDD relies on gradient descent.
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data.
Improved Active Fire Detection using Operational U-Nets
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide.
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques.
AnomalyHop: An SSL-based Image Anomaly Localization Method
An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work.
Graph-Embedded Subspace Support Vector Data Description
In this paper, we propose a novel subspace learning framework for one-class classification.
Ellipsoidal Subspace Support Vector Data Description
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification.
Boosting rare benthic macroinvertebrates taxa identification with one-class classification
One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection.
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification.
Subspace Support Vector Data Description
The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space.
