Benchmark and Survey of Automated Machine Learning Frameworks

被引：0
作者：
Zöller M.-A. [1 ]
Huber M.F. [2 ,3 ]
机构：
[1] USU Software AG, Rüppurrer Str. 1, Karlsruhe
[2] Institute of Industrial Manufacturing and Management IFF, University of Stuttgart, Allmandring 25, Stuttgart
[3] Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Nobelstr. 12, Stuttgart
来源：
Journal of Artificial Intelligence Research | 2021年 / 70卷
关键词：
Machine learning;
D O I：
10.1613/JAIR.1.11854
中图分类号：
学科分类号：
摘要：
Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites. ©2021 AI Access Foundation. All rights reserved.
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页码：409 / 472
页数：63
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