Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

Machine learning grows quickly, which has made numerous academic discoveries and is extensively evaluated in several areas. Optimization, as a vital part of machine learning, has fascinated much consideration of practitioners. The primary purpose of this paper is to combine optimization and machine learning to extract hidden rules, remove unrelated data, introduce the most productive Decision-Making Units (DMUs) in the optimization part, and to introduce the algorithm with the highest accuracy in Machine learning part. In the optimization part, we evaluate the productivity of 30 banks from eight developing countries over the period 2015-2019 by utilizing Data Envelopment Analysis (DEA). An additive Data Envelopment Analysis (DEA) model for measuring the efficiency of decision processes is used. The additive models are often named Slack Based Measure (SBM). This group of models measures efficiency via slack variables. After applying the proposed model, the Malmquist Productivity Index (MPI) is computed to evaluate the productivity of companies. In the machine learning part, we use a specific two-layer data mining filtering pre-processes for clustering algorithms to increase the efficiency and to find the superior algorithm. This study tackles data and methodology-related issues in measuring the productivity of the banks in developing countries and highlights the significance of DMUs productivity and algorithms accuracy in the banking industry by comparing suggested models.

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Feature selection using data envelopment analysis

Knowledge-Based Systems ◽

10.1016/j.knosys.2014.03.022 ◽

2014 ◽

Vol 64 ◽

pp. 70-80 ◽

Cited By ~ 21

Author(s):

Yishi Zhang ◽

Anrong Yang ◽

Chan Xiong ◽

Teng Wang ◽

Zigang Zhang

Keyword(s):

Feature Selection ◽

Data Envelopment Analysis ◽

Data Envelopment ◽

Using Data

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Distance optimization approach to ratio-form efficiency measures in data envelopment analysis

Journal of Productivity Analysis ◽

10.1007/s11123-013-0366-7 ◽

2013 ◽

Vol 42 (2) ◽

pp. 175-186 ◽

Cited By ~ 23

Author(s):

Hirofumi Fukuyama ◽

Hiroya Masaki ◽

Kazuyuki Sekitani ◽

Jianming Shi

Keyword(s):

Data Envelopment Analysis ◽

Optimization Approach ◽

Data Envelopment ◽

Efficiency Measures

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Improving Energy Efficiency of Barley Production Using Joint Data Envelopment Analysis (DEA) and Life Cycle Assessment (LCA): Evaluation of Greenhouse Gas Emissions and Optimization Approach

Sustainability ◽

10.3390/su13116082 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6082

Author(s):

Zahra Payandeh ◽

Ahmad Jahanbakhshi ◽

Tarahom Mesri-Gundoshmian ◽

Sean Clark

Keyword(s):

Energy Efficiency ◽

Life Cycle Assessment ◽

Life Cycle ◽

Energy Consumption ◽

Data Envelopment Analysis ◽

Environmental Indicators ◽

Optimization Approach ◽

Data Envelopment ◽

Aquatic Ecotoxicity ◽

Environmental Emissions

Eco-efficiency has become a cornerstone in improving the environmental and economic performance of farms. The joint use of life cycle assessment (LCA) and data envelopment analysis (DEA), known as LCA + DEA methodology, is an expanding area of research in this quest. LCA estimates the environmental impacts of the products or services, while DEA evaluates their efficiency, providing targets and benchmarks for the inefficient ones. Because energy consumption and environmental quality are highly interdependent, we carried out a study to examine energy efficiency and environmental emissions associated with rain-fed barley farms in Kermanshah Province, Iran. Fifty-four rain-fed barley farms were randomly selected, and production data were collected using questionnaires and interviews. DEA and LCA were used to quantify and compare environmental indicators before and after efficiency improvements were applied to the farms. To accomplish this, efficient and inefficient farms were identified using DEA. Then environmental emissions were measured again after inefficient farms reached the efficiency limit through management improvements. The results showed that by managing resource use, both energy consumption and environmental emissions can be reduced without yield loss. The initial amount of energy consumed averaged 13,443 MJ/ha while that consumed in the optimal state was determined to be 12,509 MJ/h, resulting in a savings of 934 MJ/ha. Based on the results of DEA, reductions in nitrogen fertilizer, diesel fuel, and phosphate fertilizer offered the greatest possibilities for energy savings. Combining DEA and LCA showed that efficient resource management could reduce emissions important to abiotic depletion (fossil fuels), human toxicity, marine aquatic ecotoxicity, global warming (GWP100a), freshwater aquatic ecotoxicity, and terrestrial ecotoxicity. This study contributes toward systematically building knowledge about crop production with the joint use of LCA + DEA for eco-efficiency assessment.

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A New Robust Dynamic Data Envelopment Analysis Approach for Sustainable Supplier Evaluation

Advances in Operations Research ◽

10.1155/2018/7625025 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20 ◽

Cited By ~ 1

Author(s):

Hava Nikfarjam ◽

Mohsen Rostamy-Malkhalifeh ◽

Abbasali Noura

Keyword(s):

Decision Making ◽

Data Envelopment Analysis ◽

Robust Optimization ◽

Supplier Selection ◽

Optimization Approach ◽

Data Envelopment ◽

Environmental Criteria ◽

Decision Making Unit ◽

Modern Business ◽

Dynamic Data Envelopment Analysis

Supplier selection is one of the intricate decisions of managers in modern business era. There are different methods and techniques for supplier selection. Data envelopment analysis (DEA) is a popular decision-making method that can be used for this purpose. In this paper, a new dynamic DEA approach is proposed which is capable of evaluating the suppliers in consecutive periods based on their inputs, outputs, and the relationships between the periods classified as desirable relationships, undesirable relationships, and free relationships with positive and negative natures. To this aim various social, economic, and environmental criteria are taken into account. A new method for constructing an ideal decision-making unit (DMU) is proposed in this paper which differs from the existing ones in the literature according to its capability of considering periods with unit efficiencies which do not necessarily belong to a unique DMU. Furthermore, the new ideal DMU has the required ability to rank the suppliers with the same efficiency ratio. In the concerned problem, the supplier that has unit efficiency in each period is selected to construct an ideal supplier. Since it is possible to have more than one supplier with unit efficiency in each period, the ideal supplier can be made with different scenarios with a given probability. To deal with such uncertain condition, a new robust dynamic DEA model is elaborated based on a scenario-based robust optimization approach. Computational results indicate that the proposed robust optimization approach can evaluate and rank the suppliers with unit efficiencies which could not be ranked previously. Furthermore, the proposed ideal DMU can be appropriately used as a benchmark for other DMUs to adjust the probable improvement plans.

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