Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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A Method of Machine Learning-based Defective Health Functional Food Detection System for Efficient Inspection of Imported Food

효율적 수입식품 검사를 위한 머신러닝 기반 부적합 건강기능식품 탐지 방법

  • Lee, Kyoungsu (Department of Big Data Analytics, Kyung Hee University) ;
  • Bak, Yerin (Department of Big Data Analytics, Kyung Hee University) ;
  • Shin, Yoonjong (School of Management, Kyung Hee University) ;
  • Sohn, Kwonsang (School of Management, Kyung Hee University) ;
  • Kwon, Ohbyung (School of Management, Kyung Hee University)
  • 이경수 (경희대학교 빅데이터응용학과) ;
  • 박예린 (경희대학교 빅데이터응용학과) ;
  • 신윤종 (경희대학교 경영학과) ;
  • 손권상 (경희대학교 경영학과) ;
  • 권오병 (경희대학교 경영학과)
  • Received : 2022.08.22
  • Accepted : 2022.09.15
  • Published : 2022.09.30
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Abstract

As interest in health functional foods has increased since COVID-19, the importance of imported food safety inspections is growing. However, in contrast to the annual increase in imports of health functional foods, the budget and manpower required for inspections for import and export are reaching their limit. Hence, the purpose of this study is to propose a machine learning model that efficiently detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export inspections data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, in order to select features for the machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. In particular, the ensemble model was the best, and it was confirmed that the derived variables and models proposed in this study can be helpful to the system used in import/export inspections.

코로나19 이후 건강기능식품의 관심이 높아짐에 따라 수입 식품 안전성 검사의 중요성도 더욱 커지고 있다. 그러나 매년 증가하는 건강기능식품 수입량과 반대로 식품 검사에 필요한 예산과 인력은 한계점에 다다르고 있다. 따라서 본 연구의 목적은 수출입 식품 중 건강기능식품을 대상으로 데이터의 특성을 살펴보고, 판별의 정확성과 결과의 설명 가능성을 고려하여 효율적으로 부적합 식품을 탐지할 수 있는 기계학습 모델 기반 자동화 시스템 설계 방안을 제시하는 것이다. 이를 위해 첫째, 부적합 판정에 영향을 미치는 식품 검사 데이터로부터 부적합 판정에 유의한 파생변수를 생성하며, 둘째, 건강기능식품 수출입 검사 데이터에 대한 탐색적 분석을 통해 클래스 불균형과 비선형성 등을 고려하여 영향변수를 선정하며, 셋째, 다양한 머신러닝 기법을 적용하여 모델 별 성능과 해석가능성에 대해 비교를 수행하고자 한다. 성능 분석 결과, 앙상블 모델이 가장 우수하였으며, 본 연구에서 제안하는 파생변수 및 모델이 수출입 식품 검사에서 활용하고 있는 시스템에 도움이 될 수 있음을 확인하였다.

Keywords

Acknowledgement

This research was supported by a grant (21163MFDS516) from Ministry of Food and Drug Safety in 2022.

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