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Value proposition operationalization in peer-to-peer platforms using machine learning

Ramos-Henríquez, José M.; Gutiérrez-Taño, Desiderio; … - In: Tourism management : research, policies, practice 84 (2021), pp. 1-12

Persistent link: https://www.econbiz.de/10012494605

A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification

Jiménez-Cordero, Asunción; Morales, Juan M.; Pineda, … - In: European journal of operational research : EJOR 293 (2021) 1, pp. 24-35

Persistent link: https://www.econbiz.de/10012502445

On the accuracy of estimators based on a binary classifier

Scholtus, Sander; Delden, Arnout van - 2020

Persistent link: https://www.econbiz.de/10012174823

Profit driven decision trees for churn prediction

Höppner, Sebastiaan; Stripling, Eugen; Baesens, Bart; … - In: European journal of operational research : EJOR 284 (2020) 3, pp. 920-933

Persistent link: https://www.econbiz.de/10012238921

Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction

Mahajan, Pravar Dilip; Maurya, Abhinav; Megahed, Aly; … - In: European journal of operational research : EJOR 285 (2020) 3, pp. 1095-1113

Persistent link: https://www.econbiz.de/10012239856

Occupational classifications: a machine learning approach

Ikudo, Akina; Lane, Julia; Staudt, Joseph; Weinberg, … - In: Journal of economic and social measurement 44 (2019) 2/3, pp. 57-87

Persistent link: https://www.econbiz.de/10012307914

Classification of monetary and fiscal dominance regimes using machine learning techniques

Hinterlang, Natascha; Hollmayr, Josef - 2020

This paper identifies U.S. monetary and fiscal dominance regimes using machine learning techniques. The algorithms are trained and verified by employing simulated data from Markov-switching DSGE models, before they classify regimes from 1968-2017 using actual U.S. data. All machine learning...