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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 703 papers

TitleStatusHype
3D Labeling Tool—0
A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks—0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection—0
A Study of Deep Learning for Network Traffic Data Forecasting—0
A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data—0
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning—0
A Unified Framework for Center-based Clustering of Distributed Data—0
An Evaluation of Classification and Outlier Detection Algorithms—0
Autoencoder Watchdog Outlier Detection for Classifiers—0
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric—0
Automated detection of business-relevant outliers in e-commerce conversion rate—0
Component-wise Adaptive Trimming For Robust Mixture Regression—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting—0
Automatic Outlier Rectification via Optimal Transport—0
An Improved Heart Disease Prediction Using Stacked Ensemble Method—0
Automatic Unsupervised Outlier Model Selection—0
Analyzing categorical time series with the R package ctsfeatures—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection—0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
Analysis of Learning from Positive and Unlabeled Data—0
Show:102550
← PrevPage 5 of 29Next →
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