PyODDS: An End-to-End Outlier Detection System

October 07, 2019 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .coveralls.yml, .gitignore, .travis.yml, LICENSE, README.md, demo.py, doc, install.sh, output, pyodds, requirements.txt, setup.py, test

Authors Yuening Li, Daochen Zha, Na Zou, Xia Hu arXiv ID 1910.02575 Category cs.LG: Machine Learning Cross-listed cs.DB, stat.ML Citations 4 Venue arXiv.org Repository https://github.com/datamllab/pyodds ⭐ 258 Last Checked 2 months ago

Abstract

PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms which meet the demands for users in different fields, w/wo data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of outlier detection algorithms, including statistical analysis and more recent deep learning based approaches. PyODDS is released under the MIT open-source license, and currently available at (https://github.com/datamllab/pyodds) with official documentations at (https://pyodds.github.io/).