Outlier Detection

Revisiting Time Series Outlier Detection: Definitions and Benchmarks

SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection

Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection. Due to the lack of ground truth labels, practitioners …

A Data Denoising Approach to Optimize Functional Clustering of Single Cell RNA-sequencing Data

AutoAudit: Mining Accounting and Time-Evolving Graphs

COPOD: Copula-Based Outlier Detection

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing unsupervised approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As …

DSR: An Accurate Single Image Super Resolution Approach for Various Degradations

Recently, convolution neural networks based approaches have achieved unprecedented success for image super resolution. However, such methods typically assume a predetermined degradation that deviates from real-world cases, resulting in poor …

SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula

Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data …

PyOD: A Python Toolbox for Scalable Outlier Detection

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural …

LSCP: Locally Selective Combination in Parallel Outlier Ensembles

In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting …

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed …