Asian Journal of Innovation and Policy

  • 제13권1호
  • /
  • Pages.1-28
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  • 2024
  • /
  • 2287-1608(pISSN)
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  • 2287-1616(eISSN)
아시아기술혁신학회 (Asian Society for Innovation and Policy)
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Integrating Machine Learning with Data Envelopment Analysis for Enhanced R&D Efficiency & Optimizing Resource Allocation in the Specialized Field

  • Seokki Cha (R&D Strategy Center, Korea Institute of Science and Technology Information (KISTI)) ;
  • Kyunghwan Park (REM Policy Team, Korea Institute of Radiological and Medical Sciences (KIRAMS))
  • 투고 : 2024.02.20
  • 심사 : 2024.05.08
  • 발행 : 2024.04.30
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초록

Enhancing the efficiency of research and development (R&D) is crucial for organizations to remain competitive and generate innovative solutions. Data Envelopment Analysis (DEA) has emerged as a powerful tool for evaluating R&D efficiency. However, traditional DEA models heavily rely on the selection of input and output variables, which can limit their effectiveness. To overcome this dependency and improve the robustness of DEA, this study proposes a novel methodology that integrates machine learning techniques with DEA for determining the most suitable input and output variables. The proposed approach is particularly relevant for specialized R&D fields, such as Radiation Emergency Medicine (REM). REM is a critical domain that deals with the medical and public health consequences of nuclear emergencies. The selection of REM as the focus of this study is motivated by several factors, including the unique challenges posed by the field, the potential for significant societal impact, and the need for efficient resource allocation in emergency situations. By leveraging machine learning algorithms, such as Support Vector Machines (SVM), the proposed methodology aims to identify the most relevant input and output variables for DEA in the context of REM. The integration of machine learning enables the DEA model to capture complex relationships and non-linearities in the data, leading to more accurate and reliable efficiency assessments. The effectiveness of the proposed methodology is demonstrated through a comprehensive evaluation using real-world REM data. The results highlight the superior performance of the machine learning-integrated DEA approach compared to traditional DEA models. This study contributes to the advancement of R&D efficiency assessment in specialized fields and provides valuable insights for decision-makers in REM and other critical domains.

키워드

과제정보

This work was supported by the Korea Institute of Science and Technology Information (K-23-L05-C02-S16).

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