Bayesian Networks Learned with Evolutionary Programming for Direct Marketing Forecasting

Research output: Other Conference ContributionsConference Paper (other)

Abstract

For direct marketing forecasting models, this study applied machine learning and stochastic optimization using Bayesian networks learned by evolutionary programming. The results of a ten-fold cross-validation experiment suggest that Bayesian networks have distinctive advantages over competing methods in accuracy of forecast, transparency of procedures, interpretability of results, and explanatory insight.
Original languageEnglish
Publication statusPublished - 2005
EventTriennial Meeting of the International Federation of the Operation Research Societies - United States, Hawaii, United States
Duration: 1 Jul 20051 Jul 2005
http://ifors.org/conference-programs/2005-program.pdf

Conference

ConferenceTriennial Meeting of the International Federation of the Operation Research Societies
Abbreviated titleIFORS Triennial Conference
CountryUnited States
CityHawaii
Period1/07/051/07/05
OtherINFORMS/IFORS
Internet address

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    CUI, G., & WONG, M. L. (2005). Bayesian Networks Learned with Evolutionary Programming for Direct Marketing Forecasting. Paper presented at Triennial Meeting of the International Federation of the Operation Research Societies, Hawaii, United States.