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http://dx.doi.org/10.5859/KAIS.2021.30.4.247

Corporate Innovation and Business Performance Prediction Using Ensemble Learning  

An, Kyung Min (동국대학교 글로벌융합연구소연구소)
Lee, Young Chan (동국대학교 정보경영학과)
Publication Information
The Journal of Information Systems / v.30, no.4, 2021 , pp. 247-275 More about this Journal
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
Ensemble Learning; LightGBM; XGboost; Catboost; Corporate Innovation; Business Performance;
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