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Application of interpretable machine learning models for the intelligent decision

文献类型：期刊论文

作者Li, Yawen1; Yang, Liu2; Yang, Bohan3; Wang, Ning4; Wu, Tian5,6,7
刊名NEUROCOMPUTING
出版日期2019-03-14
卷号333页码:273-283
ISSN号0925-2312
关键词Machine learning XGBoost R&D investments Firm size Innovation performance
DOI10.1016/j.neucom.2018.12.012
英文摘要In this study, an interpretable machine learning algorithm is proposed for the issues of intelligent decision through predicting the firms' efficiency of innovation. Based on the unbalanced panel data collected in Zhongguancun Science Parks from year 2005 to 2015, the efficiency of over 10,000 firms have been analysed in this study, and the change and growth of these firms have been captured over time. The linear regression, decision tree, random forests, neural network and XGBoost models are applied to figure out the impact factors of innovation. After comparing the results of different models, it has been found that the accuracy of XGBoost for R&D efficiency labelled, commercial efficiency labelled and overall efficiency labelled classification problems are 73.65%, 70.02% and 70.09%, which outperform the other four models. Moreover, the interpretability of XGBoost is also better than other models. Thus, the XGBoost model makes it possible for managers to predict the firm's future innovation performance derived from their innovation strategies in the current stage. Furthermore, it helps firms to build an intelligent decision support system, which is of great importance for them to deal with complex decision environments, and to increase their efficiency of innovation in the long-term dynamic competition with other firms. (C) 2018 Elsevier B.V. All rights reserved.
资助项目National Natural Science Foundation of China[71804181] ; National Center for Mathematics and Interdisciplinary Sciences, CAS
WOS研究方向Computer Science
语种英语
出版者ELSEVIER SCIENCE BV
WOS记录号WOS:000456834100025
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/32291]  
专题国家数学与交叉科学中心
通讯作者Wu, Tian
作者单位1.Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing, Peoples R China
2.Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China
3.Chinese Acad Social Sci, Grad Sch, Dept World Econ & Polit, Beijing, Peoples R China
4.China Agr Univ, Int Coll Beijing, Beijing, Peoples R China
5.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China
7.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yawen,Yang, Liu,Yang, Bohan,et al. Application of interpretable machine learning models for the intelligent decision[J]. NEUROCOMPUTING,2019,333:273-283.
APA Li, Yawen,Yang, Liu,Yang, Bohan,Wang, Ning,&Wu, Tian.(2019).Application of interpretable machine learning models for the intelligent decision.NEUROCOMPUTING,333,273-283.
MLA Li, Yawen,et al."Application of interpretable machine learning models for the intelligent decision".NEUROCOMPUTING 333(2019):273-283.

入库方式： OAI收割

来源：数学与系统科学研究院

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