The Journal of Information Systems (한국정보시스템학회지:정보시스템연구)

Korea Association of Information Systems (한국정보시스템학회)
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Corporate Innovation and Business Performance Prediction Using Ensemble Learning

앙상블 학습을 이용한 기업혁신과 경영성과 예측

  • 안경민 (동국대학교 글로벌융합연구소연구소) ;
  • 이영찬 (동국대학교 정보경영학과)
  • Received : 2021.11.23
  • Accepted : 2021.12.20
  • Published : 2021.12.31
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Abstract

Purpose This study attempted to predict corporate innovation and business performance using ensemble learning. Design/methodology/approach The ensemble techniques uses weak learning to create robust learning, which combines several weak models to derive improved performance. In this study, XGboost, LightGBM, and Catboost were used among ensemble techniques. It was compared and evaluated with traditional machine learning methods. Findings The summary of the research results is as follows. First, the type of innovation is expanding from technical innovation to non-technical areas. Second, it was confirmed that LightGBM performed best for radical innovation prediction, and XGboost performed best for incremental innovation prediction. Third, Catboost performed best for firm performance prediction. Although there was no significant difference in predictive power between ensemble techniques, we found that comparative analysis was necessary to confirm better prediction performance.

Keywords

Acknowledgement

이 논문은 2021년 대한민국 교육부와 한국연구재단의 인문사회분야 신진연구자지원사업의 지원을 받아 수행된 연구임(NRF-2021S1A5A8061237)

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