典型文献
Machine learning in data envelopment analysis:A smart mechanism for indicator selection
文献摘要：
Indicator selection has been a compelling problem in data envelopment analysis.With the advent of the big data era,scholars are faced with more complex indicator selection situations.The boom in machine learning presents an oppor-tunity to address this problem.However,poor quality indicators may be selected if inappropriate methods are used in over-fitting or underfitting scenarios.To date,some scholars have pioneered the use of the least absolute shrinkage and selec-tion operator to select indicators in overfitting scenarios,but researchers have not proposed classifying the big data scenari-os encountered by DEA into overfitting and underfitting scenarios,nor have they attempted to develop a complete indicat-or selection system for both scenarios.To fill these research gaps,this study employs machine learning methods and pro-poses a mean score approach based on them.Our Monte Carlo simulations show that the least absolute shrinkage and se-lection operator dominates in overfitting scenarios but fails to select good indicators in underfitting scenarios,while the en-semble methods are superior in underfitting scenarios,and the proposed mean approach performs well in both scenarios.Based on the strengths and limitations of the different methods,a smart indicator selection mechanism is proposed to facil-itate the selection of DEA indicators.
文献关键词：
中图分类号：
作者姓名：
Jie Wu;Yumeng Wu
作者机构：
School of Management,University of Science and Technology of China,Hefei 230026,China
文献出处：
引用格式：
[1]Jie Wu;Yumeng Wu-.Machine learning in data envelopment analysis:A smart mechanism for indicator selection)[J].中国科学技术大学学报,2022(12):38-46,67
A类：
tunity,underfitting,scenari
B类：
Machine,learning,data,envelopment,analysis,smart,mechanism,selection,Indicator,has,been,compelling,problem,With,advent,big,scholars,are,faced,more,complex,situations,boom,machine,presents,oppor,address,this,However,poor,quality,indicators,may,selected,inappropriate,methods,used,scenarios,To,date,some,have,pioneered,least,absolute,shrinkage,operator,overfitting,but,researchers,not,proposed,classifying,encountered,by,DEA,into,nor,they,attempted,develop,complete,system,both,fill,these,gaps,study,employs,poses,mean,score,approach,them,Our,Monte,Carlo,simulations,show,that,dominates,fails,good,while,semble,superior,performs,well,Based,strengths,limitations,different,facil,itate
AB值：
0.479213
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