A study on the standardization strategy for building of learning data set for machine learning applications

기계학습 활용을 위한 학습 데이터세트 구축 표준화 방안에 관한 연구

  • Choi, JungYul (Division of Computer Engineering, Sungkyul University)
  • 최정열 (성결대학교 컴퓨터공학부 부)
  • Received : 2018.07.25
  • Accepted : 2018.10.20
  • Published : 2018.10.28


With the development of high performance CPU / GPU, artificial intelligence algorithms such as deep neural networks, and a large amount of data, machine learning has been extended to various applications. In particular, a large amount of data collected from the Internet of Things, social network services, web pages, and public data is accelerating the use of machine learning. Learning data sets for machine learning exist in various formats according to application fields and data types, and thus it is difficult to effectively process data and apply them to machine learning. Therefore, this paper studied a method for building a learning data set for machine learning in accordance with standardized procedures. This paper first analyzes the requirement of learning data set according to problem types and data types. Based on the analysis, this paper presents the reference model to build learning data set for machine learning applications. This paper presents the target standardization organization and a standard development strategy for building learning data set.


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