Name
Title | ||
|---|---|---|
Modeling consumer situational choice of long distance communication with neural networks |
Abstract | ||
|---|---|---|
This study shows how artificial neural networks can be used to model consumer choice. Our study focuses on two key issues in neural network modeling - model building and feature selection. Using the cross-validation approach, we address these two issues together and specifically examine the effectiveness of a backward feature selection algorithm for consumer situational choices of communication modes. Results indicate that the proposed heuristic for feature selection is robust with respect to validation sample variation. In fact, the feature selection approach produces the same best subset of features as the all-possible-subset approach. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.dss.2007.10.009 | Decision Support Systems |
Keywords | Field | DocType |
feature selection,consumer choices,neural network,prediction risk,model building,neural network modeling,feature selection approach,artificial neural network,selection algorithm,long distance communication,all-possible-subset approach,model consumer choice,consumer situational choice,cross-validation approach,neural network model,cross validation | Heuristic,Telecommunications network,Feature selection,Computer science,Consumer choice,Model building,Model selection,Artificial intelligence,Artificial neural network,Cross-validation,Machine learning | Journal |
Volume | Issue | ISSN |
44 | 4 | Decision Support Systems |
Citations | PageRank | References |
12 | 1.26 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Y. Hu | 1 | 426 | 55.74 |
| Murali Shanker | 2 | 73 | 7.76 |
| G. Peter Zhang | 3 | 802 | 51.61 |
| Ming S. Hung | 4 | 80 | 19.29 |