Data-Enabled Analytics
Editor
- Joe Zhu(Worcester Polytechnic Institute)Vincent Charles(University of Buckingham)
Abstract
Individual chapters are listed in the "Chapters" tab
Suggested Citation
DOI: 10.1007/978-3-030-75162-3
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Book Chapters
The following chapters of this book are listed in IDEAS- Vincent Charles & Tatiana Gherman & Joe Zhu, 2021. "Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 1-29, Springer.
- Anyu Yu & Yu Shi & Joe Zhu, 2021. "Acceleration of Large-Scale DEA Computations Using Random Forest Classification," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 31-49, Springer.
- Juan Aparicio & Miriam Esteve & Jesus J. Rodriguez-Sala & Jose L. Zofio, 2021. "The Estimation of Productive Efficiency Through Machine Learning Techniques: Efficiency Analysis Trees," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 51-92, Springer.
- Chia-Yen Lee & Yu-Hsin Hung & Yen-Wen Chen, 2021. "Hybrid Data Science and Reinforcement Learning in Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 93-122, Springer.
- Bao Hoang Nguyen & Valentin Zelenyuk, 2021. "Aggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 123-158, Springer.
- Dariush Khezrimotlagh, 2021. "Parallel Processing and Large-Scale Datasets in Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 159-174, Springer.
- Hirofumi Fukuyama & William L. Weber, 2021. "Network DEA and Big Data with an Application to the Coronavirus Pandemic," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 175-197, Springer.
- Ming-Miin Yu & Kok Fong See & Bo Hsiao, 2021. "Hierarchical Data Envelopment Analysis for Classification of High-Dimensional Data," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 199-229, Springer.
- L. Calzada-Infante & S. Lozano, 2021. "Dominance Network Analysis: Hybridizing Dea and Complex Networks for Data Analytics," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 231-262, Springer.
- Hirofumi Fukuyama & Don U. A. Galagedera, 2021. "Value Extracting in Relative Performance Appraisal with Network DEA: An Application to U.S. Equity Mutual Funds," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 263-297, Springer.
- Ya Chen & Mengyuan Wang & Jingyu Yang, 2021. "Measuring Chinese Bank Performance with Undesirable Outputs: A Slack-Based Two-Stage Network DEA Approach," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 299-326, Springer.
- Wen-Min Lu & Qian Long Kweh & Mohammad Nourani & Hsiu-Fei Wang, 2021. "Using Network DEA and Grey Prediction Model for Big Data Analysis: An Application in the Global Airline Efficiency," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 327-356, Springer.
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