Data envelopment analysis with classification and regression tree – a case of banking efficiency

Data envelopment analysis with classification and regression tree – a case of banking efficiency

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Article ID: iaor20106328
Volume: 27
Issue: 4
Start Page Number: 231
End Page Number: 246
Publication Date: Sep 2010
Journal: Expert Systems
Authors: ,
Keywords: statistics: data envelopment analysis
Abstract:

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency and productivity of decision-making units (DMUs). On the other hand data mining techniques allow DMUs to explore and discover meaningful, previously hidden information from large databases. Classification and regression (C&R) is the commonly used decision tree in data mining. DEA determines the efficiency scores but cannot give details of factors related to inefficiency, especially if these factors are in the form of non-numeric variables such as operational style in the banking sector. This paper proposes a framework to combine DEA with C&R for assessing the efficiency and productivity of DMUs. The result of the combined model is a set of rules that can be used by policy makers to discover reasons behind efficient and inefficient DMUs. As a case study, we use the proposed methodology to investigate factors associated with the efficiency of the banking sector in the Gulf Cooperation Council countries.

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