Autonomic machine learning platform |
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| Institution: | 1. Dept of Computer Science, Chungbuk National University, Cheongju, Republic of Korea;2. School of Information and Communication Engineering, Chungbuk National University, Cheongju, Republic of Korea;3. Department of Computer Science, Hanyang University, Seoul, Republic of Korea;4. Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Republic of Korea;5. School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea;1. Department of Economy and Business Organization, Universitat Internacional de Catalunya, Barcelona, Spain;2. Department of Business Administration and Product Design, Universitat de Girona, Spain;3. Universidad Autónoma de Madrid, Facultad de Ciencias Económicas y Empresariales, Spain;1. Faculty of Information Technology, University of Jyväskylä, Finland;2. Institute of Information Systems and Marketing, Karlsruhe Institute of Technology, Germany;3. Gamification Group, Faculty of Information Technology and Communication Sciences, Tampere University, Finland;4. Gamification Group, Faculty of Humanities, University of Turku, Finland;1. Research Institute for Shenzhen, University of International Business and Economics, China;2. Anderson School of Management, University of New Mexico, USA;3. McLane College of Business, University of Mary Hardin-Baylor, USA;1. Department of Computer Sciences, University of Manitoba, Winnipeg, Manitoba, Canada;2. Section of Thoracic Surgery, Department of Surgery, University of Manitoba, Winnipeg, Manitoba, Canada;3. Research Institute in Oncology and Hematology, Cancer Care Manitoba, Winnipeg, Manitoba, Canada;4. Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada;1. Operations & Information Systems Department, Manning School of Business (MSB), PTB Center, 74 University Avenue, Lowell, MA 01854, USA;2. King Fahd University of Petroleum & Minerals, KFUPM Business School, Dhahran 31261, Saudi Arabia;3. Department of Management Information Systems, College of Business and Management, University of Illinois Springfield, One University Plaza, UHB 4030, Springfield, IL 62703, USA |
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| 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. |
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| Keywords: | Autonomic machine learning platform Autonomic level Machine learning Smart City |
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