“NeuralDEA” – A framework using Neural Network to re-evaluate DEA benchmarks

Bose, Amarnath; Patel, G.N.

doi:10.1007/s12597-013-0170-1

Cited by 6 publications

(17 citation statements)

References 16 publications

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“…There are three main functions (purposes) of establishing a DEA combined model. The first purpose is to reveal the function relationships between the input-output indicators of the DEA model; that is, the input and output indicators of the ANN model are exactly or partially the same as the DEA model [8,15]. The second purpose is to reveal the working principle of the DEA model [11,14,23,29]; that is, the efficiency score calculated via the DEA model is used as the output value of the machine learning model (ML) (e.g., BPNN, RF, SVR, etc.…”

Section: Literature Review 21 Dea and Its Combined Modelsmentioning

confidence: 99%

“…Therefore, Athanassopoulos and Curram [8] proposed to combine the DEA and BPNN models for efficiency analysis and conducted empirical research on the operating efficiency of the Cobb-Douglas production function and bank; the results show that the DEA model has better performance in simulating production functions, and the BPNN model is equivalent to DEA in the efficiency ranking of DMUs; they conducted indepth research on how to combine DEA and BPNN better to give full play to their respective advantages. Since then, extensive theoretical and applied research has been conducted on the combination of ANN and DEA [4,[9][10][11][12][13][14][15][16]. Additionally, other machine learning methods (such as SVR and SVM) are also combined with DEA in applied and comparative studies [4,11,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Zhong et al [13] argued that the performance of the ANN-DEA model was better than that of SVR-DEA, random forest (RF-) DEA, gradient boosting regressor (GBR-) DEA, and other models. Bose [15] established an input-oriented CCR-DEA model, took values uniformly within the output-indicator range of DEA-efficient samples, generated a certain number of modeling virtual samples, and used the output indicators of DEA as the input variables of the ANN model and the input indicators of the DEA model as the output variables of the ANN model; without a test subset, the DEA-efficient frontier ANN model was established.…”

Section: Introductionmentioning

confidence: 99%

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An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

Yu,

Lou

2023

Mathematics

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Data envelopment analysis (DEA) is a leading approach in performance analysis and discovering newer benchmarks, and the traditional DEA models cannot forecast the future efficiency of decision-making units (DMUs). Machine learning, such as the artificial neural networks (ANNs), support vector machine/regression (SVM/SVR), projection pursuit regression (PPR), etc., have been viewed as beneficial for managers in predicting system behaviors. PPR is especially suitable for small and non-normal distribution samples, the usual cases in DEA analysis. This paper integrates DEA and PPR to cover the shortcomings we faced while using DEA and DEA-BPNN, DEA-SVR, etc. This study explores the advantages of combining these complementary methods into an integrated performance measurement and prediction model. Firstly, the DEA approach is used to evaluate and rank the efficiency of DMUs. Secondly, we establish two DEA-PPR combined models to describe the DEA efficiency scores (also called the production function) and the DEA-efficient frontier function. The first combined model’s input variables are input–output indicators in the DEA model, and the output variable is the DEA efficiency. In the second model, its input variables are input or output indicators in the DEA model, and the output variable is the optimal input indicator for input-oriented DEA or the output indicator for output-oriented DEA. We conducted positive research on two examples with actual data and virtual small, medium-sized, and large samples. Compared with the DEA-BPNN and DEA-SVR models, the results show that the DEA-PPR combined model has more vital global optimization ability, better convergence, higher accuracy, and a simple topology. The DEA-PPR model can obtain robust results for both small and large cases. The DEA-BPNN and DEA-SVR models cannot obtain robust results for small and medium-sized samples due to overfitting. For large samples, the DEA-PPR model outperforms DEA-BPNN, DEA-SVR, etc. The DEA-PPR combined model possesses better suitability, applicability, and reliability than the DEA-BPNN model, the DEA-SVR model, etc.

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Section: Literature Review 21 Dea and Its Combined Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

Yu,

Lou

2023

Mathematics

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“…On the other hand, the data envelopment analysis method has been widely used in the performance assessment of various subjects [109,110]. It has been mainly used in assessing the functionalities of educational units, like schools and universities, healthcare units, like clinics and hospitals, financial units, like banks, and so forth.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

et al. 2020

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To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.

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“…In order to overcome the regression shortcoming of a single dependent variable, reference [30] estimated the efficiency of DMUs and re-evaluated DEA benchmarks by neural networks. Reference [31] established an effective benchmarking approach in DEA by clustering DMUs into a number of layers based on their efficiency and then introduced a benchmarking method through the sequence of layers.…”

Section: Review Of the Literaturementioning

confidence: 99%

Benchmarking in the Supply Chain Using Data Envelopment Analysis

Avkiran¹,

Shafiee²,

Saleh³

et al. 2018

TEL

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It is necessary to evaluate performance using a proper combination of all resources of supply chain in the best possible way to provide products and services in the market. One strategy to measure supply chain efficiency is the data envelopment analysis (DEA), which involves use of current inputs and outputs to evaluate supply chain performance. Therefore, the outcome of each DEA evaluation is not suitable for providing suitable benchmark for the future. Hence, managers are not able to improve the activities of their subset using the results of DEA model. For this purpose, we forecast data of units under evaluation using system dynamics simulation and then we present a proper model to formulate strategies for improving performance using the proposed DEA model. Finally, we implemented the designed algorithm in the milk industry of Fars province (Iran), and proper strategies for improving the efficiency of this industry were developed.

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“NeuralDEA” – A framework using Neural Network to re-evaluate DEA benchmarks

Cited by 6 publications

References 16 publications

An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

Benchmarking in the Supply Chain Using Data Envelopment Analysis

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