This paper speeds up OCSSVM training using SMO.
problem Training One-Class Slab SVMs is slow.
method Uses updated SMO to divide large problems into smaller, analytically solvable subproblems.
result Training OCSSVMs scales better with large datasets.
Proposed SMO algorithm for OC-SVM+ significantly outperforms non-sequential algorithms.
problem One-class SVM with privileged information
method Sequential Minimal Optimization (SMO) algorithm
result Finite-time convergence established
New SVM feature selection methods improve wafer testing accuracy.
problem Need for improved SVM feature selection methods.
method Entropy measurement and K-medoid clustering for feature selection.
result One-class SVM's enhance wafer testing accuracy.
A new method explains anomalies in one-class models using deep Taylor decomposition.
problem Understanding why one-class models classify data points as anomalies.
method Recompose one-class SVM as a neural network, perform deep Taylor decomposition.
result The method reliably explains a wide set of data anomalies and outperforms baselines.
A simple SVM model accurately infers overlapping clusters across various datasets.
problem Overlapping clusters in people, words, and books.
method A simple one-class SVM model that works for various overlapping clustering models.
result Simple SVM yields accurate and scalable inference for overlapping clusters.
Proposes OC-NN for anomaly detection in complex data.
problem Detect anomalies in complex data sets.
method Combines deep learning and one-class objective for anomaly detection.
result OC-NN performs on par with state-of-the-art methods and outperforms conventional methods.
Optimizes one-class classification methods for better performance.
problem Improving one-class classification accuracy through hyperparameter optimization.
method Hyperparameter optimization for five one-class classification methods (SVM, NND, LNND, LOF, ALP) using various datasets.
result ALP and SVM perform best after hyperparameter optimization, with ALP being more efficient.
KOC+ uses privileged information to improve one-class classification performance.
problem Outlier detection and novelty detection using kernel methods.
method Kernel ridge regression with correction function for privileged information.
result KOC+ achieves better generalization performance compared to traditional methods.
Proposes AE-1SVM for scalable anomaly detection.
problem Optimization complexity and scalability issues in OC-SVM for large datasets.
method Combines autoencoder with random Fourier features and stochastic gradient descent for end-to-end training.
result End-to-end training achieves better performance than previous methods.
A novel OC-SVM calibration method improves change point detection in time series.
problem Identifying change points in system health status using time series data.
method Heuristic search method to find optimal input data and hyperparameters for OC-SVM.
result OC-SVM can detect change points in time series with fewer training data, achieving satisfactory accuracy.
ALP outperforms other data descriptors in one-class classification.
problem Challenges in one-class classification using data descriptors.
method Determined optimal default hyperparameters for data descriptors, proposed ALP, evaluated using leave-one-dataset-out procedure.
result ALP outperforms other data descriptors, including IF and SVM.
One-Class Boundary Peeling detects outliers efficiently and robustly.
problem Unsupervised outlier detection in diverse data distributions.
method One-Class Boundary Peeling uses flexible boundaries generated by one-class SVMs and iteratively peels them.
result One-Class Boundary Peeling outperforms state-of-the-art methods in synthetic data simulations.
FROCC uses random projections for fast one-class classification.
problem Efficient one-class classification for large datasets.
method Random projection of data onto random unit vectors, with region bounding.
result Significant improvement in ROC performance with substantial speedup.
A number of important applied problems in engineering, finance and medicine can be formulated as a problem of anomaly detection. A classical approach to the problem is to describe a normal state using a one-class support vector machine. Then to detect anomalies we quantify a distance from a new observation to the const…
DROCC improves anomaly detection across various domains without requiring domain-specific transformations.
problem Anomaly detection in structured domains like images and tabular data.
method DROCC assumes class points lie on a low-dimensional manifold and uses a robust loss function to avoid representation collapse.
result DROCC achieves up to 20% higher accuracy than state-of-the-art methods across multiple domains.
SONAR improves outlier detection for streaming data with strong theoretical guarantees.
problem Outlier detection for non-stationary streaming data with high Type I/II errors.
method SONAR is an efficient SGD-based OCSVM solver with strong convex regularization and lifelong learning guarantees.
result SONAR outperforms traditional OCSVM in Type I/II error rates under non-stationary data.
The paper develops methods for novelty detection on path space using signature-based statistics.
problem Novelty detection on path space as a hypothesis testing problem.
method Signature-based test statistics, transportation-cost inequalities, CVaR, one-class SVM algorithms.
result Established lower bounds on type-II error and general power bounds. This paper defends SVMs against poisoning attacks using DBSCAN and hardness proofs.
problem Adversarial injection of specially crafted samples into training data to misclassify SVMs.
method Two strategies: robust SVM algorithms and data sanitization (DBSCAN).
result Proves hardness of simple SVM problem and effectiveness of DBSCAN for poisoning attacks.
Proposes a novel model-agnostic training procedure for anomaly detection incorporating known anomalies.
problem Challenges of anomaly detection, especially when only a few anomalous samples are available.
method Reformulates one-class classification as a binary classification problem, using pseudo-anomalous samples drawn from a normalizing flow model.
result Demonstrates comparable or superior performance on tasks with variable amounts of known anomalies.
A new method detects outliers in dirty data using a leave-out strategy.
problem Outliers in training data skew support vector machine performance.
method Leave-out strategy: temporarily omit one candidate at a time for outlier detection.
result The leave-out strategy improves outlier detection compared to existing methods.
OneFlow detects anomalies by finding a minimal volume region, outperforming other methods.
problem Anomaly detection in data with complex outlier structures.
method Flow-based one-class classifier that uses a minimal volume region to define outliers.
result OneFlow outperforms other methods in real-world anomaly detection tasks.
Proposes a novel SVM model for binary classification with different misclassification costs.
problem Real-world classification problems with varying misclassification costs.
method Incorporates performance constraints in SVM formulation to seek a hyperplane with maximal margin and misclassification rates below given thresholds.
result The proposed model gives users control over misclassification rates in one class at the expense of the other.
A new method combines OCSVM with representation learning for UAD.
problem Detect anomalies without labeled data, especially in rare or unavailable cases.
method Custom loss formulation that aligns latent features with OCSVM decision boundary.
result Succeeds in detecting small, non-hyperintense lesions in MRI.
Anomaly detection with LSTM neural networks for variable-length data.
problem Anomaly detection in variable-length data sequences.
method LSTM neural networks, OC-SVM, SVDD, gradient and quadratic programming.
result Significant performance gains over conventional methods.
There is a broad range of BioNLP tasks for which active learning (AL) can significantly reduce annotation costs and a specific AL algorithm we have developed is particularly effective in reducing annotation costs for these tasks. We have previously developed an AL algorithm called ClosestInitPA that works best with tas…
The two-sample hypothesis testing problem is studied for the challenging scenario of high dimensional data sets with small sample sizes. We show that the two-sample hypothesis testing problem can be posed as a one-class set classification problem. In the set classification problem the goal is to classify a set of data …
A general approach for anomaly detection or novelty detection consists in estimating high density regions or Minimum Volume (MV) sets. The One-Class Support Vector Machine (OCSVM) is a state-of-the-art algorithm for estimating such regions from high dimensional data. Yet it suffers from practical limitations. When appl…
Context improves one-class classifiers in dynamic data streams.
problem Improving one-class classification in data streams with limited training data.
method Proposes using context to guide one-class classifier learning in data streams, presenting three frameworks.
result The use of context can improve the performance of streaming one-class classifiers.
A new active learning method for one-class classification using two classifiers.
problem Reducing manual labeling efforts in one-class classification.
method Uses two one-class classifiers for active learning, proposing new query strategies.
result Improved results compared to existing methods on various datasets.
DOC3 learns from contradictions to improve deep one class classification.
problem Deep one class classification problems.
method Formalizes learning from contradictions for one class large-margin loss, proposes DOC3 algorithm.
result DOC3 incurs lower generalization error compared to traditional inductive learning.
A new method learns features for one-class classification using intra-class splitting.
problem Challenges in one-class classification due to limited normal class samples.
method Intra-class splitting and joint training of typical and atypical samples with loss functions.
result The method outperforms other models in one-class classification tasks.
Support spinor machine extends SVM to handle spinor fields in time series data.
problem Handling nonstationary and nonlinear time series data for classification.
method Using wedge product to extend vector fields to spinor fields, extending SVM to support spinor machine.
result Support spinor machine outperforms SVM in one class classification of physiological time series data.
The paper proposes a novel MKL approach for OCC using ℓp-norm constraints.
problem Addressing the MKL problem for one-class classification.
method A min-max saddle point Lagrangian optimisation problem is formulated and solved efficiently.
result The proposed method outperforms baselines and other algorithms on various data sets.
A multi-layer KRR Auto-Encoder architecture for one-class classification.
problem One-class classification in machine learning.
method Multi-layer architecture of Kernel Ridge Regression Auto-Encoders with semi-supervised learning.
result Experimental results show the superiority of the proposed MKOC over existing one-class classifiers.
Paper adapts SVDD for 3D-shapes filtering and outlier detection.
problem Filtering and detecting outliers in 3D-shapes.
method Adapted SVDD to SimpleMKL, developing Slim-MK-SVDD for tighter boundaries.
result Slim-MK-SVDD produces a tighter boundary around data.
Proposes OCGNN for detecting anomalies in graph data.
problem Detecting anomalies in graph-structured data.
method One Class Graph Neural Network (OCGNN) combining Graph Neural Networks and one-class classification.
result Significant improvements in anomaly detection compared to baselines.
OCLEP+ detects anomalies and intrusions with short patterns.
problem Detecting anomalies and intrusions in minimal data.
method One-class Classification using Length statistics of Emerging Patterns Plus.
result Effective detection of anomalies and intrusions with minimal data.
A novel multi-layer architecture for one-class classification using graph-embedded kernel ridge regression.
problem Outlier detection in one-class classification using only normal samples.
method Stacking various Graph-Embedded Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion.
result The proposed method outperforms existing one-class classifiers on 21 benchmark datasets.
A new classifier improves one-class predictions on unevenly sampled data.
problem Non-uniformly sampled data affects one-class classifier performance.
method Dynamic decision boundary based on minimum spanning tree.
result Proves effectiveness and robustness compared to state-of-the-art classifiers.
Random Forests (RFs) are strong machine learning tools for classification and regression. However, they remain supervised algorithms, and no extension of RFs to the one-class setting has been proposed, except for techniques based on second-class sampling. This work fills this gap by proposing a natural methodology to e…
Extends OC-KSR for multi-task one-class classification.
problem Improving one-class classification performance with shared information.
method Linear and non-linear structure learning mechanisms for multi-task one-class classification.
result Improved performance on multiple one-class problems.
This paper uses deep neural networks for one-class classification by splitting normal data into typical and atypical subsets.
problem Training deep neural networks with only one class of data for one-class classification.
method Intra-class splitting to create typical and atypical subsets, using binary loss and auxiliary subnetworks.
result The method outperformed seven baselines and had comparable performance to state-of-the-art methods on image datasets.
Enhances robustness of one-class classification framework.
problem Susceptibility to training data corruptions and inability to rank observations.
method Regularization of null-space kernel Fisher methodology in OC-KSR.
result Enhanced robustness against contamination in training set.
This article compares and evaluates active learning methods for outlier detection with one-class classifiers.
problem Selecting an appropriate active learning method for outlier detection with one-class classifiers is challenging due to diverse assumptions and objectives.
method The article categorizes various methods, proposes evaluation criteria, and conducts extensive experiments to compare them.
result Formulated guidelines for selecting active learning methods for outlier detection with one-class classifiers.
Method improves few-shot one-class classification.
problem Learning binary classifier with data from only one class.
method Modified MAML algorithm to learn initialization for few-shot OCC.
result Method leads to better results than classical approaches.
FCDD explains deep anomaly detection by mapping anomalies away and providing heatmap explanations.
problem Deep one-class classification's non-linear transformation makes it hard to interpret.
method FCDD learns a mapping that concentrates nominal samples, maps anomalies away, and provides heatmap explanations.
result FCDD sets a new state of the art in unsupervised anomaly detection on MVTec-AD.
The paper optimizes autoencoder latent spaces for one-class learning with controlled connectivity.
problem Learning representations with controllable connectivity for better upstream tasks.
method A novel loss function based on persistent homology controls the connectivity of autoencoder latent spaces.
result The controlled connectivity in latent space improves one-class learning performance, especially in low sample size scenarios.
One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods based on extreme learn…