基于DEA和决策树的区域创新效率评价与分析基于DEA和决策树的区域创新效率评价与分析.pdf

Management Science and Engineering 管理科学与工程, 2016, 5(4), 186-192 Published Online December 2016 in Hans. /journal/mse /10.12677/mse.2016.54020 Evaluation and Analysis of the Regional Innovation Efficiencies Based on DEA and Decision Tree Zhizong Chen School of Economics and Management, Tongji University, Shanghai rd th th Received: Dec. 3 , 2016; accepted: Dec. 19 , 2016; published: Dec. 26 , 2016 Copyright © 2016 by author and Hans Publishers Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). /licenses/by/4.0/ Open Access Abstract To scientifically and objectively evaluate and analyze the efficiencies of regional innovation sys- tems is a good base to analyze and make policies of regional innovation. Data Envelopment Analy- sis (DEA) is one of the most used methods for evaluating the efficiency of regional innovation sys- tems (which are referred to Decision Making Units). Decision tree model is a tree structure, which is a process of classification based on feature; it is an important, useful and visual tool of data mining. Combining the advantages of DEA and decision tree model, this paper presents a hybrid model to evaluate and analyze the efficiencies of regional innovation systems using DEA and deci- sion tree method. Based on the evaluation of the efficiencies of 31 regional innovation systems in China, the classification decision tree is established and its role in strategic decision making (or policy making) of regional innovation systems is analyzed. Keywords DEA, Decision Tree, Innovation Efficiencies Evaluation, Regional Innovation System 基于DEA和决策树的区域创新效率评价与分析 陈志宗 同济大学经济与管理学院，上海