论文标题
outlierDetection.jl：朱莉娅编程语言的模块化异常检测生态系统
OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language
论文作者
论文摘要
OutlierDetection.jl是朱莉娅（Julia）异常检测的开源生态系统。它提供了直接在朱莉娅实施的一系列高性能异常检测算法。与以前的软件包相反，我们的生态系统可以使用高级编程语言进行开发高度估计的离群检测算法。此外，它为将来的离群检测算法提供了标准化但灵活的界面，并允许在先前的软件包中看不见模型组合物。在整个生态系统中都执行了最佳实践，例如单元测试，连续集成和代码覆盖范围报告。 OutlierDetection.jl的最新版本可在https://github.com/outlierdetectionjl/outlierdetection.jl上找到。
OutlierDetection.jl is an open-source ecosystem for outlier detection in Julia. It provides a range of high-performance outlier detection algorithms implemented directly in Julia. In contrast to previous packages, our ecosystem enables the development highly-scalable outlier detection algorithms using a high-level programming language. Additionally, it provides a standardized, yet flexible, interface for future outlier detection algorithms and allows for model composition unseen in previous packages. Best practices such as unit testing, continuous integration, and code coverage reporting are enforced across the ecosystem. The most recent version of OutlierDetection.jl is available at https://github.com/OutlierDetectionJL/OutlierDetection.jl.
