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2019 JMLR JMLR 2019

PyOD: A Python Toolbox for Scalable Outlier Detection

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 https://github.com/yzhao062/pyod. [abs] [ pdf ][ bib ] [ code ] © JMLR 2019. (edit, beta)

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Yue Zhao , Zain Nasrullah , Zheng Li

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Unsupervised Learning Data Science & Analytics > Applications > Clustering

Keywords

anomaly detection outlier detection ensemble method scalable algorithm neural network
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