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Verifying the Classification Accuracy for Korea's Standardized Classification System of Research F&E by using LDA(Linear Discriminant Analysis)

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키워드

주제분류경영학

초록

Recently, research F&E(Facilities and Equipment) have become very important as tools and means to lead the development of science and technology. The government has been continuously expanding investment budgets for R&D and research F&E, and the need for efficient operation and systematic management of research F&E built up nationwide has increased. In December 2010, The government developed and completed a standardized classification system for national research F&E. However, accuracy and trust of information classification are suspected because information is collected by a method in which a user(researcher) directly selects and registers a classification code in NTIS. Therefore, in the study, we analyzed linearly using linear discriminant analysis(LDA) and analysis of variance(ANOVA), to measure the classification accuracy for the standardized classification system(8 major-classes, 54 sub-classes, 410 small-classes) of the national research facilities and equipment established in 2010, and revised in 2015. For the analysis, we collected and used the information data(50,271 cases) cumulatively registered in NTIS(National Science and Technology Service) for the past 10 years. This is the first case of scientifically verifying the standardized classification system of the national research facilities and equipment, which is based on information of similar classification systems and a few expert reviews in the in-outside of the country. As a result of this study, the discriminant accuracy of major-classes organized hierarchically by sub-classes and small-classes was 92.2 %, which was very high. However, in post hoc verification through analysis of variance, the discrimination power of two classes out of eight major-classes was rather low. It is expected that the standardized classification system of the national research facilities and equipment will be improved through this study.

Abstract

Recently, research F&E(Facilities and Equipment) have become very important as tools and means to lead the development of science and technology. The government has been continuously expanding investment budgets for R&D and research F&E, and the need for efficient operation and systematic management of research F&E built up nationwide has increased. In December 2010, The government developed and completed a standardized classification system for national research F&E. However, accuracy and trust of information classification are suspected because information is collected by a method in which a user(researcher) directly selects and registers a classification code in NTIS. Therefore, in the study, we analyzed linearly using linear discriminant analysis(LDA) and analysis of variance(ANOVA), to measure the classification accuracy for the standardized classification system(8 major-classes, 54 sub-classes, 410 small-classes) of the national research facilities and equipment established in 2010, and revised in 2015. For the analysis, we collected and used the information data(50,271 cases) cumulatively registered in NTIS(National Science and Technology Service) for the past 10 years. This is the first case of scientifically verifying the standardized classification system of the national research facilities and equipment, which is based on information of similar classification systems and a few expert reviews in the in-outside of the country. As a result of this study, the discriminant accuracy of major-classes organized hierarchically by sub-classes and small-classes was 92.2 %, which was very high. However, in post hoc verification through analysis of variance, the discrimination power of two classes out of eight major-classes was rather low. It is expected that the standardized classification system of the national research facilities and equipment will be improved through this study.

참고문헌 (18)

  1. Cho, H. H, "Study on Writing National Science Technology Standardized Classification Table" (2001), Science & Technology Policy Institute
  2. Daniela M. W, "Penalized classification using Fisher's linear discriminant", Journal of the Royal Statistical Society : Series B(Statistical Methodology), 73(5), pp.53~772 (2011)
  3. Dipillo P, "The application of bias to discriminant analysis", Communication in Statistics Theory and Methodology, A5, pp.843~854 (1976)
  4. Dudoit S, "Comparison of discrimination methods for the classification of tumors using gene expression data", Journal of the American Statistical Association, 97(457), pp.77~87 (2002)
  5. Duintjer T. J, "Improving implementation of linear discriminant analysis for the high dimension", small sample size problem, 2007, pp.52(1), 423~437
  6. Guo Y, "Regularized linear discriminant analysis and its application in microarray", Biostatistics, 8(1), pp.86~100 (2007)
  7. Hong, S. K, "Study on classification criteria of national R&D projects for systematic information provision" (2016), Korea Institute of Science &Technology Evaluation and Planning
  8. Lee C. Y, "Mutually Exclusive and Collectively Exhaustive Feature Selection Scheme", Applied Soft Computing, 7(68), pp.961~971 (2018)
  9. 이동걸, "기술경영 연구 분야의 연구 분류체계 제시에 관한 연구 - 연구 설계와 연구 방법론을 중심으로 -", 경영과 정보연구, 35(4), pp.269~287 (2016), 대한경영정보학회[DOI]
  10. Lee H. S, "Manual for SPSS 14.0" (2009), Beob Moon Sa
  11. Ministry of Science and ICT, "Manual on the Management of National R&D Facilities and Equipment" (2019), Ministry of Science and ICT
  12. the Standardized Classification System for Research Facilities and Equipment, "National Research Facilities and Equipment Center", Korea Basic Science Institute, ~, pp.PRISM Research Report(22) (2015)
  13. OECD, "Guidelines for collecting and reporting data on research and experimental development, Frascati Manual" (2015)
  14. Seol, S. S, "Theory and Practice of Knowledge Activities Classification" (2000), Hannam University Press
  15. Sin, T. Y, "R&D for Korean Technology Classification System" (1994), Science & Technology Policy Institute
  16. 성봉석, "유전자은행의 운영성과 제고를 위한 유전자원이용촉진 판별요인의 탐색", 경영과 정보연구, 35(3), pp.95~113 (2016), 대한경영정보학회[DOI]
  17. 성태경, "표준과 기술혁신의 연관성에 관한 소고", 경영과 정보연구, 29(4), pp.225~244 (2010), 대한경영정보학회[DOI]
  18. Yoo, J. Y, "the National Science and Technology Standardized Classification System" (2018), Korea Institute of Science & Technology Evaluation and Planning

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