TY - JOUR T1 - Data driven approach for weight restricted data envelopment analysis models with single output AU - Kurt, Şenol AU - Yüksel, Mustafa Kerem AU - Dinçergök, Burcu PY - 2023 DA - December Y2 - 2023 DO - 10.56554/jtom.1333333 JF - Journal of Turkish Operations Management JO - JTOM PB - METE GÜNDOĞAN WT - DergiPark SN - 2630-6433 SP - 1768 EP - 1779 VL - 7 IS - 2 LA - en AB - This study aims to explore whether a machine learning algorithm can be used to make improvements in assessing unit efficiencies via a data envelopment analysis (DEA) model. In this study, a DEA model is used to calculate the efficiency scores of Desicion Making Units (DMUs). Then, an ML algorithm is trained that aims to predict the single output using inputs. Ranking of input features based on relative feature importance values obtained from the trained ML model is fed to the DEA model as weight restrictions. As a result, the two DEA models are compared with each other. ML-based insights (feature importance ranking) improve the DEA model in the direction of fewer zero weights. The additional weight restrictions are data depdendent, and hence realistic. As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model. KW - DEA KW - ML KW - ANN CR - Appiahene, P., Missah, Y. M., & Najim, U. (2020). Predicting bank operational efficiency using machine learning algorithm: comparative study of decision tree, random forest, and neural networks. Advances in fuzzy systems, 2020, 1-12. doi: https://doi.org/10.1155/2020/8581202 CR - Adler, A. I., & Painsky, A. (2022). Feature importance in gradient boosting trees with cross-validation feature selection. Entropy, 24(5), 687. doi: https://doi.org/10.3390/e24050687 CR - Atici, K. B. (2012). Using data envelopment analysis for the efficiency and elasticity evaluation of agricultural farms (Doctoral dissertation, University of Warwick). 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UR - https://doi.org/10.56554/jtom.1333333 L1 - https://dergipark.org.tr/tr/download/article-file/3289492 ER -