Outlier Detection
195 papers with code • 11 benchmarks • 11 datasets
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.
Benchmarks
These leaderboards are used to track progress in Outlier Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Outlier Detection models and implementations
Fashion-MNIST
MVTecAD
ImageNet-O
pathbased
Ecoli
SKAB
Most implemented papers
TOD: GPU-accelerated Outlier Detection via Tensor Operations
yzhao062/pytod
•
•
Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection.
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
yzhao062/pyod
•
•
To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the "rare events" that appear in the tails of a distribution.
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution
Directed evolution is a versatile technique in protein engineering that mimics the process of natural selection by iteratively alternating between mutagenesis and screening in order to search for sequences that optimize a given property of interest, such as catalytic activity and binding affinity to a specified target.
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
automl/deepcave
•
•
Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
pygod-team/pygod
•
•
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
Computationally Assisted Quality Control for Public Health Data Streams
However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams.
A Fast Greedy Algorithm for Outlier Mining
The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data.
Condition Number Analysis of Kernel-based Density Ratio Estimation
JohnYKiyo/density_ratio_estimation
•
•
We show that the kernel least-squares method has a smaller condition number than a version of kernel mean matching and other M-estimators, implying that the kernel least-squares method has preferable numerical properties.
Anomaly Detection via oversampling Principal Component Analysis
Based on this idea, an over-sampling principal component analysis outlier detection method is proposed for emphasizing the influence of an abnormal instance (or an outlier).
A Framework for Clustering Uncertain Data
The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community.
