Python

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 …

SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from multiple sources when direct collection is difficult or costly. Although it is a fundamental step for many data science …

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 …

Combining Machine Learning Models Using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to …

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 …

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles