Autonomic machine learning platform

Keon Myung Lee, Jaesoo Yoo, Sang Wook Kim, Jee Hyong Lee, Jiman Hong

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Acquiring information properly through machine learning requires familiarity with the available algorithms and understanding how they work and how to address the given problem in the best possible way. However, even for machine-learning experts in specific industrial fields, in order to predict and acquire information properly in different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the application of machine learning. For non-experts, it is much more difficult to make accurate predictions through machine learning. In this paper, we propose an autonomic machine learning platform which provides the decision factors to be made during the developing of machine learning applications. In the proposed autonomic machine learning platform, machine learning processes are automated based on the specification of autonomic levels. This autonomic machine learning platform can be used to derive a high-quality learning result by minimizing experts’ interventions and reducing the number of design selections that require expert knowledge and intuition. We also demonstrate that the proposed autonomic machine learning platform is suitable for smart cities which typically require considerable amounts of security sensitive information.

Original languageEnglish
Pages (from-to)491-501
Number of pages11
JournalInternational Journal of Information Management
Volume49
DOIs
StatePublished - 2019 Dec

Keywords

  • Autonomic level
  • Autonomic machine learning platform
  • Machine learning
  • Smart City

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