Data-Enabled Analytics

Edited by Joe Zhu () and Vincent Charles ()

in International Series in Operations Research and Management Science from Springer, currently edited by Camille C. Price, Joe Zhu and Frederick S. Hillier

Date: 2021
ISBN: 978-3-030-75162-3
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Chapters in this book:

Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis

Vincent Charles, Tatiana Gherman and Joe Zhu

Acceleration of Large-Scale DEA Computations Using Random Forest Classification

Anyu Yu, Yu Shi and Joe Zhu

The Estimation of Productive Efficiency Through Machine Learning Techniques: Efficiency Analysis Trees

Juan Aparicio, Miriam Esteve, Jesus J. Rodriguez-Sala and José Zofío

Hybrid Data Science and Reinforcement Learning in Data Envelopment Analysis

Chia-Yen Lee, Yu-Hsin Hung and Yen-Wen Chen

Aggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives

Bao Hoang Nguyen and Valentin Zelenyuk

Parallel Processing and Large-Scale Datasets in Data Envelopment Analysis

Dariush Khezrimotlagh

Network DEA and Big Data with an Application to the Coronavirus Pandemic

Hirofumi Fukuyama and William L. Weber

Hierarchical Data Envelopment Analysis for Classification of High-Dimensional Data

Ming-Miin Yu, Kok Fong See and Bo Hsiao

Dominance Network Analysis: Hybridizing Dea and Complex Networks for Data Analytics

L. Calzada-Infante and S. Lozano

Value Extracting in Relative Performance Appraisal with Network DEA: An Application to U.S. Equity Mutual Funds

Hirofumi Fukuyama and Don Galagedera

Measuring Chinese Bank Performance with Undesirable Outputs: A Slack-Based Two-Stage Network DEA Approach

Ya Chen, Mengyuan Wang and Jingyu Yang

Using Network DEA and Grey Prediction Model for Big Data Analysis: An Application in the Global Airline Efficiency

Wen-Min Lu, Qian Long Kweh, Mohammad Nourani and Hsiu-Fei Wang

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DOI: 10.1007/978-3-030-75162-3

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