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Application of interpretable machine learning models for the intelligent decision
Li, Yawen1; Yang, Liu2; Yang, Bohan3; Wang, Ning4; Wu, Tian5,6,7
2019-03-14
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume333Pages:273-283
AbstractIn 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.
KeywordMachine learning XGBoost R&D investments Firm size Innovation performance
DOI10.1016/j.neucom.2018.12.012
Language英语
Funding ProjectNational Natural Science Foundation of China[71804181] ; National Center for Mathematics and Interdisciplinary Sciences, CAS
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000456834100025
PublisherELSEVIER SCIENCE BV
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/32292
Collection国家数学与交叉科学中心
Affiliation1.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
Recommended Citation
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.
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