C4.5: programs for machine learning
C4.5: programs for machine learning
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
A penalty-function approach for pruning feedforward neural networks
Neural Computation
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Toward a hybrid data mining model for customer retention
Knowledge-Based Systems
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Feature selection in bankruptcy prediction
Knowledge-Based Systems
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
An information delivery model for banking business
International Journal of Information Management: The Journal for Information Professionals
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This paper describes a business intelligence application of neural networks in analyzing consumer heterogeneity in the context of eating-out behavior in Taiwan. We apply a neural network rule extraction algorithm which automatically groups the consumers into identifiable segments according to their socio-demographic information. Within each of these segments, the consumers are distinguished between those who eat-out frequently from those who do not based on their psychological traits and eat-out considerations. The data set for this study has been collected through a survey of 800 Taiwanese consumers. Demographic information such as gender, age and income were recorded. In addition, information about their psychological traits and eating-out considerations that might influence the frequency of eating-out were obtained. The results of our data analysis show that the neural network rule extraction algorithm is able to find distinct consumer segments and predict the consumers within each segment with good accuracy.