디지털융복합연구 (Journal of Digital Convergence)

  • 제16권10호
  • /
  • Pages.205-212
  • /
  • 2018
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

기계학습 활용을 위한 학습 데이터세트 구축 표준화 방안에 관한 연구

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

  • 최정열 (성결대학교 컴퓨터공학부 부)
  • Choi, JungYul (Division of Computer Engineering, Sungkyul University)
  • 투고 : 2018.07.25
  • 심사 : 2018.10.20
  • 발행 : 2018.10.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

고성능 CPU/GPU의 개발과 심층신경망 등의 인공지능 알고리즘, 그리고 다량의 데이터 확보를 통해 기계학습이 다양한 응용 분야로 확대 적용되고 있다. 특히, 사물인터넷, 사회관계망서비스, 웹페이지, 공공데이터로부터 수집된 다량의 데이터들이 기계학습의 활용에 가속화를 가하고 있다. 기계학습을 위한 학습 데이터세트는 응용 분야와 데이터 종류에 따라 다양한 형식으로 존재하고 있어 효과적으로 데이터를 처리하고 기계학습에 적용하기에 어려움이 따른다. 이에 본 논문은 표준화된 절차에 따라 기계학습을 위한 학습 데이터세트를 구축하기 위한 방안을 연구하였다. 먼저 학습 데이터세트가 갖추어야할 요구사항을 문제 유형과 데이터 유형별로 분석하였다. 이를 토대로 기계학습 활용을 위한 학습 데이터세트 구축에 관한 참조모델을 제안하였다. 또한 학습 데이터세트 구축 참조모델을 국제 표준으로 개발하기 위해 대상 표준화 기구의 선정 및 표준화 전략을 제시하였다.

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.

키워드

참고문헌

  1. O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis & K. Taha. (2015). Efficient machine learning for big data: A review. Big Data Research, 2(3), 87-93. https://doi.org/10.1016/j.bdr.2015.04.001
  2. F. Cady. (2017). The data science handbook. Wiley.
  3. S. Kim & Y. Jeong. (2017). Machine Learning. Seoul : Hanbit Media.
  4. A. Moore & M. S. Lee. (1988). Cached sufficient statistics for efficient machine learning with large datasets. Journal of Artificial Intelligence Research, 8, 67-91.
  5. S. Lee. (2017) Artificial Intelligence: Recent Artificial Intelligence Development Trend and Future Evolution Direction. LG Economic Research Institute(Online). http://www.lgeri.com/report/view.do?idx=19584
  6. Open Data Portal, https://www.data.go.kr/
  7. Seoul Open Data Plaza, http://data.seoul.go.kr/
  8. Gyeonggi Data Dream, https://data.gg.go.kr/portal/mainPage.do
  9. Healthcare Bigdata Hub, http://opendata.hira.or.kr/
  10. T. Borovicka, M. Jirina Jr, P. Kordik & M. Jirina. (2012). Selecting representative data sets. Advances in data mining knowledge discovery and applications, InTech. 43-70.
  11. A. Ferrari, G. O. Spagnolo & S. Gnesi. (2017). Towards a Dataset for Natural Language Requirements Processing, Proceedings of International Working Conference on Requirements Engineering: Foundation for Software Quality.
  12. ICT Standardization Strategy Map ver.2018. (2017). Telecommunications Technology Association.
  13. TTAK.KO-10.0974, Big Data - Definition, Concept and Use Cases of Data Providing Service, TTA Standards. (2016)
  14. TTAK.KO-10.0975, Big Data - Requriements and Functional Architecture for Data Providing Service. TTA Standards, 2016.
  15. ITU-T SG20 TR.AI4IoT, Artificial Intelligence and Internet of Thing (work in progress), 2018
  16. ISO/IEC AWI TR 20547-1, Information technology -- Big data reference architecture -- Part 1: Framework and application process
  17. ISO/IEC AWI 22989, Artificial Intelligence Concepts and Terminology (work in progress),
  18. ISO/IEC AWI 23053, Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML) (work in progress)
  19. List of datasets for machine learning research, https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research, Wikipedia, 2018.
  20. T. Bertin-Mahieux, D. P. W. Ellis, B. Whitman, & P. Lamere. (2011). The Million Song Dataset. Proceedings of the 12th International Society for Music Information Retrieval Conference, 2(9), 591-596.
  21. S. J. Raudys & A. K. Jain. (1991). Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(3), 252-264. https://doi.org/10.1109/34.75512
  22. G. Pant, P. Srinivasan & F. Menczer. (2004). Crawling the web. Web Dynamics, 153-177.
  23. J. Morcos, Z. Abedjan, I. F. Ilyas, M. Ouzzani, P. Papotti & M. Stonebraker. (2015). Dataxformer: An interactive data transformation tool. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 883-888.
  24. Y. Li, R. Krishnamurthy, S. Raghavan, S. Vaithyanathan & H. V. Jagadish. (2008). Regular expression learning for information extraction. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 21-30.
  25. I. Guyon & A. Elisseeff. (2006). An introduction to feature extraction. In Feature extraction. Springer, 1-25.
  26. T. Fushiki. (2011). Estimation of prediction error by using K-fold cross-validation. Statistics and Computing, 21(2), 137-146. https://doi.org/10.1007/s11222-009-9153-8