Guest Editorial: AutoML for Nonstationary Data

IEEE Transactions on Artificial Intelligence, vol. 5, pp. 2456–2457

Abstract

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.

Authors 6

1. Ran Cheng

   Southern University of Science and Technology

   Affiliation as printed

   Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

2. Hugo Jair Escalante

   National Institute of Astrophysics, Optics and Electronics

   Affiliation as printed

   Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

3. Wei-Wei Tu
   Affiliation as printed

   4Paradigm Inc., Beijing, China

4. Jan N. van Rijn Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, RA, The Netherlands

5. Shuo Wang

   University of Birmingham

   Affiliation as printed

   School of Computer Science, The University of Birmingham, Birmingham, U.K

6. Yun Yang

   Yunnan University

   Affiliation as printed

   National Pilot School of Software, Yunnan University, Kunming, China