OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language

Autor:	Muhr, David, Affenzeller, Michael, Blaom, Anthony D.
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning I.2.5
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.04550 Zobrazit plný text záznamu View this record from Arxiv