PyOD: A Python Toolbox for Scalable Outlier Detection

Published in

Journal of machine learning research(2019)

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TL;DR

PyOD provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers.

Abstract

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 network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox's development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or this https URL.

Authors

Yue Zhao

11 papers

Zain Nasrullah

3 papers

Zheng Li

3 papers

References33 items

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

AlphaClean: Automatic Generation of Data Cleaning Pipelines

Anomaly Detection for an E-commerce Pricing System

LSCP: Locally Selective Combination in Parallel Outlier Ensembles

PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP)

PyOD: A Python Toolbox for Scalable Outlier Detection

Published in

Journal of machine learning research(2019)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Yue Zhao

11 papers

Zain Nasrullah

3 papers

Zheng Li

3 papers

TL;DR

Abstract

Authors

References33 items

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

AlphaClean: Automatic Generation of Data Cleaning Pipelines

Anomaly Detection for an E-commerce Pricing System

LSCP: Locally Selective Combination in Parallel Outlier Ensembles

PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP)

TL;DR

Abstract

Authors

References33 items

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

AlphaClean: Automatic Generation of Data Cleaning Pipelines

Anomaly Detection for an E-commerce Pricing System

LSCP: Locally Selective Combination in Parallel Outlier Ensembles

PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP)

Generative Adversarial Active Learning for Unsupervised Outlier Detection

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images

A survey of anomaly detection techniques in financial domain

Theoretical Foundations and Algorithms for Outlier Ensembles

Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction

RapidMiner: Data Mining Use Cases and Business Analytics Applications

Fast and reliable anomaly detection in categorical data

Scikit-learn: Machine Learning in Python

Visual Evaluation of Outlier Detection Models

Anomaly-based network intrusion detection: Techniques, systems and challenges

Isolation Forest

Angle-based outlier detection in high-dimensional data

Feature bagging for outlier detection

One-class support vector machines - an application in machine fault detection and classification

Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator

Time-series novelty detection using one-class support vector machines

Discovering cluster-based local outliers

LOCI: fast outlier detection using the local correlation integral

Estimating the Support of a High-Dimensional Distribution

LOF: identifying density-based local outliers

Efficient algorithms for mining outliers from large data sets

Independent consultant

Histogram-based Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm

Interpreting and Unifying Outlier Scores

Analyzing ranking data using decision tree

A Novel Anomaly Detection Scheme Based on Principal Component Classifier

Future Generation Computer Systems

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