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 201–225 of 703 papers

TitleStatusHype
Deep Sequence Modeling for Anomalous ISP Traffic Prediction—0
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images—0
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory—0
A Non-Parametric Control Chart For High Frequency Multivariate Data—0
Toward Scalable and Unified Example-based Explanation and Outlier Detection—0
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction—0
Clustering with Outlier Removal—0
Detecting abnormal events in video using Narrowed Normality Clusters—0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
Detecting outliers by clustering algorithms—0
Detecting Outliers in Data with Correlated Measures—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
Detecting Surprising Situations in Event Data—0
Detecting Unusual Input-Output Associations in Multivariate Conditional Data—0
Detection of Abnormal Input-Output Associations—0
A Unified Framework for Center-based Clustering of Distributed Data—0
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Detect Professional Malicious User with Metric Learning in Recommender Systems—0
Anomaly Rule Detection in Sequence Data—0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
Differentially Private Analysis of Outliers—0
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search—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