Autonomic machine learning platform

Citations

WEB OF SCIENCE

50
Citations

SCOPUS

69

초록

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.

키워드

Autonomic machine learning platformAutonomic levelMachine learningSmart CityE-learningLearning systemsSmart cityAccurate predictionAutonomic levelDecision factorsDesign selectionsExpert knowledgeIndustrial fieldsMachine learning applicationsSensitive informationsMachine learning
제목
Autonomic machine learning platform
저자
Lee, Keon MyungYoo, JaesooKim, Sang-WookLee, Jee-HyongHong, Jiman
DOI
10.1016/j.ijinfomgt.2019.07.003
발행일
2019-12
유형
Article
저널명
International Journal of Information Management
49
페이지
491 ~ 501