南方科技大学知识苑(SUSTech KC): Guest Editorial: AutoML for Nonstationary Data

题名	Guest Editorial: AutoML for Nonstationary Data
作者	Ran Cheng1 ; Hugo Jair Escalante 2; Wei-Wei Tu 3; Jan N. Van Rijn 4; Shuo Wang 5; Yun Yang 6
发表日期	2024-06-01
DOI	10.1109/TAI.2024.3387583
发表期刊	IEEE Transactions on Artificial Intelligence 影响因子和分区
ISSN	2691-4581
卷号	5 期号:6
摘要	The five papers in this special section address different aspects of automated machine learning (AutoML) from fundamental algorithms to real-world applications. Developing high-performance machine learning models is a difficult task that usually requires expertise from data scientists and knowledge from domain experts. To make machine learning more accessible and ease the labor-intensive trial-and-error process of searching for the most appropriate machine learning algorithm and the optimal hyperparameter setting, AutoML was developed and has become a rapidly growing area in recent years. AutoML aims at automation and efficiency of the machine learning process across domains and applications. Nowadays, data is commonly collected over time and susceptible to changes, such as in Internet-of-Things (IoT) systems, mobile phone applications and healthcare data analysis. It poses new challenges to the traditional AutoML with the assumption of data stationarity. Interesting research questions arise around whether, when and how to effectively and efficiently deal with non-stationary data in AutoML.
相关链接	[IEEE记录]
收录类别	EI
学校署名	第一
引用统计
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/783786
专题	工学院_计算机科学与工程系
作者单位	1.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China 2.Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico 3.4Paradigm Inc., Beijing, China 4.Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, RA, The Netherlands 5.School of Computer Science, The University of Birmingham, Birmingham, U.K. 6.National Pilot School of Software, Yunnan University, Kunming, China
第一作者单位	计算机科学与工程系
第一作者的第一单位	计算机科学与工程系
推荐引用方式 GB/T 7714	Ran Cheng,Hugo Jair Escalante,Wei-Wei Tu,et al. Guest Editorial: AutoML for Nonstationary Data[J]. IEEE Transactions on Artificial Intelligence,2024,5(6).
APA	Ran Cheng,Hugo Jair Escalante,Wei-Wei Tu,Jan N. Van Rijn,Shuo Wang,&Yun Yang.(2024).Guest Editorial: AutoML for Nonstationary Data.IEEE Transactions on Artificial Intelligence,5(6).
MLA	Ran Cheng,et al."Guest Editorial: AutoML for Nonstationary Data".IEEE Transactions on Artificial Intelligence 5.6(2024).