Improved customer choice predictions using ensemble methods

DOI10.1016/J.EJOR.2006.05.029zbMath1121.90387OpenAlexW2144492955MaRDI QIDQ872292

Rob Potharst, Michiel C. van Wezel

Publication date: 27 March 2007

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: http://repub.eur.nl/pub/1943

zbMATH Keywords

marketingdata miningCARTbaggingboostingchoice modelsensemblesbrand choicebias/variance decomposition

Mathematics Subject Classification ID

Marketing, advertising (90B60)

Related Items (6)

Improved customer choice predictions using ensemble methods ⋮ Profit driven decision trees for churn prediction ⋮ De-noising option prices with the wavelet method ⋮ A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction ⋮ A combination selection algorithm on forecasting ⋮ Exchange rate forecasting using ensemble modeling for better policy implications

Uses Software

• UCI-ml
• AdaBoost.MH

Cites Work

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• Explaining consumer choice through neural networks: the stacked generalization approach
• A flexible brand choice model based on neural net methodology
• Periodic Time Series Models
• The elements of statistical learning. Data mining, inference, and prediction

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