Autonomic machine learning platform. (December 2019)

• Record Type: Journal Article
• Authors: Lee, Keon Myung; Yoo, Jaesoo; Kim, Sang-Wook; Lee, Jee-Hyong; Hong, Jiman
• Is Part Of: International journal of information management. Volume 49(2019)
• Page Start: 491
• Page End: 501
• Publication Date: 2019-12
• Subjects: Autonomic machine learning platform -- Autonomic level -- Machine learning -- Smart City; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2019.07.003
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: Autonomic machine learning platform. Design and architecture of the Autonomic machine learning platform. Derive high-quality learning results by minimizing expert's interventions and design selection. Perform machine learning at various autonomic levels without additional security systems. 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.