Efficient neural network ensembles detect outliers.
problem Outlier detection in neural networks.
method Variational Bayesian neural networks with gradient descent sampling.
result Outlier detection results comparable to other methods.
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48 results for “outlier ensembles”
A new framework selects best outlier detectors locally for improved ensemble performance.
problem Challenges in combining outlier detectors without ground truth.
method Locally Selective Combination in Parallel Outlier Ensembles (LSCP) framework.
result LSCP_AOM variant consistently outperforms other methods on real-world datasets.
A new method detects outliers using ensembles of Dirichlet process mixtures.
problem Challenges in unsupervised outlier detection using Dirichlet process mixtures.
method Ensembles of Dirichlet process Gaussian mixtures with random subspace and subsampling.
result Empirically outperforms existing approaches in unsupervised outlier detection.
BAE uses boosting to improve autoencoder ensembles for robust outlier detection.
problem Overfitting in autoencoders limits their effectiveness in unsupervised outlier detection.
method Boosting-based Autoencoder Ensemble (BAE) trains autoencoders sequentially with weighted sampling to reduce outliers and inject diversity.
result BAE outperforms state-of-the-art approaches in various outlier detection conditions.
Ensemble methods for classification and clustering have been effectively used for decades, while ensemble learning for outlier detection has only been studied recently. In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing err…
New graph-based method selects outlier ensemble components.
problem Poor components negatively affect consensus results in outlier ensembles.
method Mapping rankings to graphs, mining to identify subsets.
result Our method outperforms state-of-the-art techniques.
Enhances predictive models against misspecification and outliers.
problem Suboptimal generalization under misspecification and outliers.
method Combines PAC ensemble bounds with a generalized logarithm score function.
result Produces predictive distributions resistant to both misspecification and outliers.
XGBOD combines unsupervised and supervised methods for better outlier detection.
problem Enhanced outlier detection from normal observations in various datasets.
method Hybrid approach using unsupervised representation learning to improve a supervised classifier.
result XGBOD outperforms competing methods across seven datasets.
Proposes an ensemble framework for outlier detection with embedded feature selection.
problem Improving performance of outlier detection in noisy data.
method Unified feature selection and outlier detection, thresholded self-paced learning, alternate algorithm.
result The proposed framework ODEFS outperforms existing methods on real-world datasets.
DCSO dynamically selects top-performing base detectors for outlier ensembles.
problem Challenges in selecting and combining outlier scores from different detectors.
method DCSO dynamically selects top-performing base detectors based on local k-nearest neighbors.
result DCSO provides consistent performance improvement over static combination approaches.
KOD detects outliers in high-dimensional data.
problem Challenges in outlier detection in high-dimensional settings.
method Kernel transformation followed by projection pursuit approach with ensemble of directions and result combination.
result Empirical evaluations show effectiveness on various datasets.
Bayesian LSTM for outlier detection reduces overfitting.
problem Overfitting and lack of uncertainty in LSTM networks.
method Approximate Bayesian estimation with Ensemble Kalman Filter and maximum likelihood.
result The method reduces overfitting and provides uncertainty estimates.
Study examines error correction in ECG segmentation neural networks.
problem Improving accuracy of ECG segmentation using neural networks.
method Training deep convolutional neural networks for ECG segmentation and analyzing ensemble errors.
result Outliers in ensemble can be used to evaluate data representation quality.
MFRDE uses medians of forest estimators to robustly estimate densities in noisy data.
problem Robust density estimation in the presence of outliers.
method MFRDE uses pointwise median operation on forest density estimators fitted on subsampled datasets.
result MFRDE achieves robustness against all outliers while maintaining accuracy for density estimation.
This paper evaluates how well outlier detection methods work with samples of data.
problem Resilience of outlier detection methods to sampling.
method Estimating resilience to sampling of outlier detection methods and their ensembles.
result Outlier detection methods are not equally resilient to sampling, and careful selection of sampling scheme and method is necessary.
PyOD offers scalable outlier detection for multivariate data.
problem Scalable outlier detection for multivariate data.
method Wide range of outlier detection algorithms, including ensembles and neural networks.
result Robust and scalable outlier detection.
An Ensemble Anomaly Detection Framework for Risk Calculation Integrity
problem Detecting errors in risk valuation outputs
method Ensemble Quality Assessment Framework (EQAF)
result Achieves F1 scores of 61-79% across four datasets
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.
Robust Kalman filtering method for outlier detection.
problem Outliers and misspecified measurement models in state-space models.
method Combines generalised Bayesian inference with Kalman filters for robustness and efficiency.
result Matches or outperforms other robust filtering methods at lower computational cost.
Approach detects outliers in large datasets for credit card fraud.
problem Lack of patterns and changing fraudulent patterns make fraud detection difficult.
method Ensemble of clustering methods to assign consistency scores to data points.
result Area under precision-recall curve is a better evaluation metric for outlier detection.
An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Taskscs.LG
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which l…
Deep learning and entropy methods detect skin cancer outliers.
problem Detecting an outlier class in a skin cancer dataset.
method Ensemble of 13 CNNs, two outlier handling approaches, and meta-data integration.
result Successfully addressed the outlier class and improved diagnostic accuracy.
Paper improves RLS for sparse outlier detection in linear models.
problem Outliers contaminate linear regression models infrequently.
method Hierarchical-optimization recursive least squares with sparsity-inducing regularization.
result The method robustly estimates linear filters/systems with outliers.
Approach detects outliers in large data sets by consistent data points.
problem Lack of training data for supervised learning models.
method Two-phase approach: first phase identifies non-outliers, second phase uses one-class classifier.
result Quickly retrieves samples for consistent and non-outlier data sets.
OCCER uses regression models to classify data points as outliers.
problem One-class classification problem where training data is only from the target class.
method OCCER converts OCC to many regression problems, computes outlier scores from regression errors.
result OCCER outperforms state-of-the-art algorithms on various datasets.
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effectstat.ML
ALTBI enhances outlier detection by maximizing the inlier-memorization effect.
problem Improving outlier detection models via optimization of inlier-memorization effect.
method ALTBI introduces two techniques: increasing mini-batch size and using adaptive threshold for truncated loss function.
result ALTBI achieves state-of-the-art performance in identifying outliers with lower computation costs.
Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfercond-mat.dis-nn
The study examines spectral dynamics in deep neural networks, predicting how outliers evolve during training.
problem Understanding spectral evolution in deep neural networks during training.
method Developed a two-level dynamical mean-field theory (DMFT) to track spectral dynamics.
result The theory predicts how outliers evolve with training time, width, output scale, and initialization variance.
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.
Stabilizes online learning by using weighted reservoir sampling.
problem Real-world deployment sensitivity to outliers causes low accuracy in final solutions.
method Weighted reservoir sampling to stabilize ensemble model without additional data passes.
result Risk of ensemble classifier is bounded with respect to the underlying online learning method's regret.
Survey of AI-based outlier detection methods for various domains.
problem Detecting unusual events to prevent infrastructure damage.
method Categorizes six major OD methods: Statistical, Distance, Density, Clustering, Learning, and Ensemble.
result Advances and challenges of each method discussed.
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
problem Challenges in classical SPC for high-dimensional, non-Gaussian data.
method Hybrid VAE architecture with ARD prior, ensemble filtering, and changepoint detection.
result VSCOUT achieves superior sensitivity to special-cause structure and controlled false alarms.
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spacesstat.ML
Tree ensemble kernels improve Bayesian optimization for mixed features and constraints.
problem Optimizing over mixed-feature spaces with known constraints.
method Kernel interpretation of tree ensembles as Gaussian Process prior, compatible optimization formulation for acquisition function, integration of known constraints.
result Framework outperforms state-of-the-art methods for mixed-feature spaces and constraints.
New matrix ensembles better match deep neural network spectral densities.
problem Theoretical spectral density models for deep networks do not match empirical observations.
method Introduced new matrix ensemble classes to better fit observed spectral densities.
result Theoretical models for deep networks are significantly flawed.
Combines Laplace approximations of deep networks for better uncertainty quantification.
problem Overconfident predictions on outliers in deep learning models.
method Gaussian mixture model posterior using weighted sum of Laplace approximations of pre-trained deep networks.
result Mitigates overconfidence 'far away' from training data.
KNN ensembles improve Tweedie regression performance.
problem Improving regression models for Tweedie distribution.
method Combining bagged features, bagged observations, and varied k in KNN ensembles.
result Varying k provides significant improvements in model fit and robustness.
Single model estimates ensemble uncertainty efficiently.
problem Efficient uncertainty quantification in deep learning models.
method Contextual similarity distillation, approximating ensemble variance with a single model.
result Single model estimates predictive variance with a single forward pass.
Single model estimates uncertainty via biased data shifts.
problem Estimating uncertainties in deep neural networks.
method Trivial input transformation to approximate ensemble behavior.
result Single model uncertainty estimates are superior to current methods.
Methods for unsupervised anomaly detection suffer from the fact that the data is unlabeled, making it difficult to assess the optimality of detection algorithms. Ensemble learning has shown exceptional results in classification and clustering problems, but has not seen as much research in the context of outlier detecti…
A new ensemble method reduces noise by prioritizing hard instances.
problem Noise and outliers in training data affect ensemble methods' performance.
method Probability of instance selection inversely proportional to its hardness.
result Accuracy significantly better in high noise scenarios.
DCASE 2021 ASD task tackles domain-shifted anomalous sound detection.
problem Detecting unknown anomalous sounds under domain-shifted conditions.
method Ensemble of outlier exposure and inlier modeling detectors, feature learning from machine identification.
result Two types of remarkable approaches were adopted by top teams.
Two VAE-based methods improve anomaly detection in semi-supervised settings.
problem Anomaly detection with limited labeled data.
method Intuitive idea of separating latent vectors for normal and outlier data, derived from probabilistic formulations.
result Marked improvement in outlier detection compared to state-of-the-art methods.
Improves predictive algorithm performance with domain adaptation.
problem Improves performance of predictive algorithms in distributional shift scenarios.
method Domain adaptive bootstrap aggregating with iterative nearest neighbor sampling.
result Proposes a method to improve predictive algorithm performance in distributional shift scenarios.
ReWTS ensemble improves time-series forecasting by adapting to changing dynamics.
problem Complex, multi-faceted, evolving data in process industries.
method Chunk-based, recency-weighted temporal segmentation of data for multi-step forecasting.
result Significantly outperforms conventional models in mean squared forecasting error.
POTATOES improves autoencoder UOD accuracy without tuning.
problem Improving unsupervised outlier detection accuracy.
method Randomly partition data, overfit each part with an autoencoder, use max reconstruction error as anomaly score.
result Significant improvement in UOD performance for dense inlier sets.
RFX-Fuse combines Breiman and Cutler's Random Forest with modern ML capabilities.
problem Lack of a unified ML engine with diverse capabilities.
method Unified ML engine with native GPU/CPU support, delivering 5+ functionalities in one model.
result Native explainable similarity and imputation validation.
Two new scoring methods improve anomaly detection in Isolation Forest.
problem Anomaly and outlier detection in data.
method Generalized score function and volume-based scoring for individual trees.
result Significant improvement in anomaly detection on 34 datasets.
Random Matrix Theory explains loss surface Hessians in neural networks.
problem Understanding the loss surfaces of neural networks.
method Investigation of local spectral statistics of neural network Hessians.
result Excellent agreement with Gaussian Orthogonal Ensemble statistics.
CAIRO separates ranking from scaling to improve robustness.
problem Conflating ranking and scaling in regression leads to model vulnerability.
method Two-stage approach: first learns a scoring function, then recovers scale.
result CAIRO recovers true regression function with auto-calibration guarantees.