Bayesian Networks Learned with Evolutionary Programming for Direct Marketing Forecasting

Geng CUI; Man Leung WONG

Bayesian Networks Learned with Evolutionary Programming for Direct Marketing Forecasting

Research output: Other Conference Contributions › Conference Paper (other) › Other Conference Paper › peer-review

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 language	English
Publication status	Published - 2005
Event	Triennial Meeting of the International Federation of the Operation Research Societies - United States, Hawaii, United States Duration: 1 Jul 2005 → 1 Jul 2005 http://ifors.org/conference-programs/2005-program.pdf

Conference

Conference	Triennial Meeting of the International Federation of the Operation Research Societies
Abbreviated title	IFORS Triennial Conference
Country/Territory	United States
City	Hawaii
Period	1/07/05 → 1/07/05
Other	INFORMS/IFORS
Internet address	http://ifors.org/conference-programs/2005-program.pdf

Cite this

@conference{8e3192da9d29449280f86ad601f9db7e,

title = "Bayesian Networks Learned with Evolutionary Programming for Direct Marketing Forecasting",

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.",

author = "Geng CUI and WONG, \{Man Leung\}",

year = "2005",

language = "English",

note = "Triennial Meeting of the International Federation of the Operation Research Societies, IFORS Triennial Conference ; Conference date: 01-07-2005 Through 01-07-2005",

url = "http://ifors.org/conference-programs/2005-program.pdf",

}

Bayesian Networks Learned with Evolutionary Programming for Direct Marketing Forecasting. / CUI, Geng ; WONG, Man Leung.
2005. Paper presented at Triennial Meeting of the International Federation of the Operation Research Societies, Hawaii, Hawaii, United States.

Research output: Other Conference Contributions › Conference Paper (other) › Other Conference Paper › peer-review

TY - CONF

T1 - Bayesian Networks Learned with Evolutionary Programming for Direct Marketing Forecasting

AU - CUI, Geng

AU - WONG, Man Leung

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

M3 - Conference Paper (other)

T2 - Triennial Meeting of the International Federation of the Operation Research Societies

Y2 - 1 July 2005 through 1 July 2005

ER -

Bayesian Networks Learned with Evolutionary Programming for Direct Marketing Forecasting

Abstract

Conference

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