DOI QR코드

DOI QR Code

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

Abstract

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.

Keywords

Machine learning;Artificial Intelligence;Reference model;Standardization;Learning data set

References

  1. G. Pant, P. Srinivasan & F. Menczer. (2004). Crawling the web. Web Dynamics, 153-177.
  2. 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.
  3. 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.
  4. I. Guyon & A. Elisseeff. (2006). An introduction to feature extraction. In Feature extraction. Springer, 1-25.
  5. 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
  6. Gyeonggi Data Dream, https://data.gg.go.kr/portal/mainPage.do
  7. Healthcare Bigdata Hub, http://opendata.hira.or.kr/
  8. 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.
  9. 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.
  10. ICT Standardization Strategy Map ver.2018. (2017). Telecommunications Technology Association.
  11. TTAK.KO-10.0974, Big Data - Definition, Concept and Use Cases of Data Providing Service, TTA Standards. (2016)
  12. TTAK.KO-10.0975, Big Data - Requriements and Functional Architecture for Data Providing Service. TTA Standards, 2016.
  13. ITU-T SG20 TR.AI4IoT, Artificial Intelligence and Internet of Thing (work in progress), 2018
  14. ISO/IEC AWI TR 20547-1, Information technology -- Big data reference architecture -- Part 1: Framework and application process
  15. ISO/IEC AWI 22989, Artificial Intelligence Concepts and Terminology (work in progress),
  16. ISO/IEC AWI 23053, Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML) (work in progress)
  17. List of datasets for machine learning research, https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research, Wikipedia, 2018.
  18. 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.
  19. Open Data Portal, https://www.data.go.kr/
  20. Seoul Open Data Plaza, http://data.seoul.go.kr/
  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.
  22. 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.
  23. F. Cady. (2017). The data science handbook. Wiley.
  24. S. Kim & Y. Jeong. (2017). Machine Learning. Seoul : Hanbit Media.
  25. A. Moore & M. S. Lee. (1988). Cached sufficient statistics for efficient machine learning with large datasets. Journal of Artificial Intelligence Research, 8, 67-91.
  26. 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