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 401–425 of 703 papers

TitleStatusHype
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders—0
Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE—0
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection—0
WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining—0
What do we learn? Debunking the Myth of Unsupervised Outlier Detection—0
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!—0
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing—0
Robust outlier detection by de-biasing VAE likelihoods—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets—0
Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation—0
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies—0
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation—0
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Exploring Outliers in Crowdsourced Ranking for QoE—0
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest—0
Fairness-aware Outlier Ensemble—0
Fair Outlier Detection—0
Feature Engineering for Scalable Application-Level Post-Silicon Debugging—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