SOTAVerified

Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

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Showing 301–325 of 703 papers

TitleStatusHype
Interpreting Outliers in Time Series Data through Decoding Autoencoder—0
GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting—0
GBG++: A Fast and Stable Granular Ball Generation Method for Classification—0
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search—0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
Byzantine-Resilient Secure Federated Learning—0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey—0
Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series—0
Clustering with Outlier Removal—0
Generic Outlier Detection in Multi-Armed Bandit—0
Geometric Tight Frame based Stylometry for Art Authentication of van Gogh Paintings—0
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
GWQ: Gradient-Aware Weight Quantization for Large Language Models—0
HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI—0
Improving Solar Flare Prediction by Time Series Outlier Detection—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Hierarchical Multiresolution Feature- and Prior-based Graphs for Classification—0
Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression—0
HLoOP -- Hyperbolic 2-space Local Outlier Probabilities—0
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection—0
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation—0
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All datasetsECG5000Balance scale_class 1Breast cancer Wisconsin_class 2Breast cancer Wisconsin_class 4Fashion-MNISTGlass identificationHeart-CHepatitisInternet AdIonosphere_class bSKAB

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified