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 451–475 of 703 papers

TitleStatusHype
Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey—0
Outlier Detection in Indoor Localization and Internet of Things (IoT) using Machine Learning—0
Component-wise Adaptive Trimming For Robust Mixture Regression—0
Consistent and Flexible Selectivity Estimation for High-Dimensional DataCode0
Fair Outlier Detection—0
Variational Hyper-Encoding Networks—0
Adaptive Double-Exploration Tradeoff for Outlier Detection—0
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series—0
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data—0
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning—0
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies—0
A review on outlier/anomaly detection in time series data—0
SUOD: Toward Scalable Unsupervised Outlier DetectionCode1
Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation Maximization—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Outlier Detection Ensemble with Embedded Feature Selection—0
Explainable outlier detection through decision tree conditioningCode1
Outlier Detection and Data Clustering via Innovation Search—0
Minimal Solutions for Relative Pose with a Single Affine Correspondence—0
Event Outlier Detection in Continuous TimeCode0
Active Learning of SVDD Hyperparameter Values—0
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation LearningCode0
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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