Guest Editorial: AutoML for Nonstationary Data

Ran Cheng, Hugo Jair Escalante, Wei-Wei Tu, Jan N.Van Rijn, Shuo Wang, Yun Yang*

*Corresponding author for this work

Research output: Contribution to journalEditorialpeer-review

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.

Original languageEnglish
Pages (from-to)2456-2457
Number of pages2
JournalIEEE Transactions on Artificial Intelligence
Volume5
Issue number6
DOIs
Publication statusPublished - 25 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Special issues and sections
  • Automation
  • Machine learning
  • Mobile handsets
  • Data models
  • Data analysis
  • Machine learning algorithms

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

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