The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Operation Plan of Big Data Prediction Model using Cut-off-Voting Classifier in Administrative Big Data Environment

행정 빅데이터 환경에서 컷오프-투표 분류기를 활용한 빅데이터 예측모형의 실험

  • Woosik Lee (Research Center, Korea Social Security Information Service)
  • 이우식 (한국사회보장정보원 사회보장정보연구소)
  • Received : 2024.03.04
  • Accepted : 2024.04.20
  • Published : 2024.05.31
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Abstract

In order to operate predictive models utilizing administrative big data, it is crucial to consider policy changes and the characteristics of highly volatile data. Considering this scenario, this study proposes the Cut-off Voting Classifier (CVC) algorithm. This proposed algorithm prevents a sharp decline in accuracy by utilizing multiple weak classifiers. The study validates the proposed algorithm's performance through experiments. The performance evaluation demonstrates the ability to maintain stable prediction rates even in situations with a sharp decline in predictive model accuracy.

행정 빅데이터를 활용하는 예측 모형을 운영하기 위해서는 정책의 변화 및 변동성 심한 데이터의 특성이 고려가 되어야만 한다. 이런 상황을 고려하여 본 연구에서는 Cut-off Voting Classifier(CVC) 알고리즘을 제안한다. 제안하는 알고리즘은 여러개의 약 분류기를 활용하여 적중률이 급격하게 하락하는 것을 방지하는 알고리즘이다. 본 연구에서는 제안하는 알고리즘을 실험을 통해 성능을 검증한다. 성능검증 결과 급격하게 예측모형 적중률이 하락하는 상황에서도 안정적으로 예측률을 유지한다는 것을 입증할 수 있었다.

Keywords

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