Current Issue
DATA REDUCTION BY ENTROPY MEASURE OF FACTOR IMPORTANCE IN DATA ENVELOPMENT ANALYSIS
DATA REDUCTION BY ENTROPY MEASURE OF FACTOR IMPORTANCE IN DATA ENVELOPMENT ANALYSIS
Chao-Chin Chao*
Department of Food and Beverage Management, Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan, R.O.C.
Tel: +886-7-6222131 ext 51184
*Corresponding E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Data Envelopment Analysis (DEA) is a mathematical programming approach for measuring relative efficiencies within a group of decision making units based on multiple inputs and outputs. However, the number of inputs and outputs affects the discrimination level in efficiency evaluation of DMUs. This study introduces the concept of entropy to measure the importance of factors and uses the entropy values to identify the omitted factor(s). This method enables the decision maker to determine the less influential factor for omission between two highly correlated factors. It does not only improve the discrimination in the DEA evaluation but also retain information for further ranking.
Keywords: Data envelopment analysis, Data reduction, Entropy, Correlation
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