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DEA efficiency prediction based on IG–SVM

  • Machine Learning - Applications & Techniques in Cyber Intelligence
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Abstract

The traditional data envelopment analysis (DEA) model is widely used for efficiency evaluation of decision making unit composed of past or current input–output data in production system. We propose a new efficiency prediction model which for the first time combines information granulation (IG) and support vector machine (SVM) with DEA model, to evaluate the future efficiency of decision making unit in this paper. The model first uses fuzzy information granulation to granulate the input–output data over time series and then describe the characteristics of the data within each time window by the minimum, average and maximum values and establish the IG–SVM model based on the fuzzy information granulation and support vector machine. After training by time series data, the optimal model of regression is built. Based on this model, the minimum, average and maximum values of the next window in the future are predicted, and finally the future efficiency of the decision making unit can be calculated by the DEA model. Case studies show the feasibility and applicability of the proposed method.

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Acknowledgements

The authors acknowledge the support of the project “the research on supply-side structural reform of manufacture enterprise under Internet + background (16BGL015)” funded by National Social science foundation of China and the project of “Research on Shaanxi Provincial science and technology innovation efficiency, mode and approach (2016D040)” funded by Shaanxi Social science foundation.

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  1. School of Management, Xi’an University of Architecture and Technology, Xi’an, 710055, China

    Quan Zhang & Chengjun Wang

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  1. Quan Zhang
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  2. Chengjun Wang
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Correspondence to Quan Zhang.

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Zhang, Q., Wang, C. DEA efficiency prediction based on IG–SVM. Neural Comput & Applic 31, 8369–8378 (2019). https://doi.org/10.1007/s00521-018-3904-4

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