PyOD: A Python']Python Toolbox for Scalable Outlier Detection

被引：0
|
作者：
Zhao, Yue [1 ]
Nasrullah, Zain [2 ]
Li, Zheng [3 ]
机构：
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] Univ Toronto, Toronto, ON M5S 2E4, Canada
[3] Northeastern Univ Toronto, Toronto, ON M5X 1E2, Canada
来源：
JOURNAL OF MACHINE LEARNING RESEARCH | 2019年 / 20卷
关键词：
anomaly detection; outlier detection; outlier ensembles; neural networks; machine learning; data mining; !text type='Python']Python[!/text; SUPPORT;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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页数：7
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