Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mailing decisions in the catalog sales industry
Management Science
IEEE Transactions on Pattern Analysis and Machine Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Direct Marketing Performance Modeling Using Genetic Algorithms
INFORMS Journal on Computing
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Business Intelligence: An Analysis of the Literature
Information Systems Management
Graph self-organizing maps for cyclic and unbounded graphs
Neurocomputing
Electronic Commerce Research and Applications
Bayesian variable selection for binary response models and direct marketing forecasting
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
Two New Prediction-Driven Approaches to Discrete Choice Prediction
ACM Transactions on Management Information Systems (TMIS)
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
Direct marketing decision support through predictive customer response modeling
Decision Support Systems
Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM Investment
Journal of Management Information Systems
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Machine learning methods are powerful tools for data mining with large noisy databases and give researchers the opportunity to gain new insights into consumer behavior and to improve the performance of marketing operations. To model consumer responses to direct marketing, this study proposes Bayesian networks learned by evolutionary programming. Using a large direct marketing data set, we tested the endogeneity bias in the recency, frequency, monetary value (RFM) variables using the control function approach; compared the results of Bayesian networks with those of neural networks, classification and regression tree (CART), and latent class regression; and applied a tenfold cross-validation. The results suggest that Bayesian networks have distinct advantages over the other methods in accuracy of prediction, transparency of procedures, interpretability of results, and explanatory insight. Our findings lend strong support to Bayesian networks as a robust tool for modeling consumer response and other marketing problems and for assisting management decision making.