Name
Title | ||
|---|---|---|
A principled approach for building and evaluating neural network classification models |
Abstract | ||
|---|---|---|
In this paper, we propose a principled approach to building and evaluating neural network classification models for decision support system (DSS) implementations. First, the usefulness of neural networks for use with e-commerce data and for Bayesian classification is discussed. Next, the theory concerning model accuracy and generalization is presented. Then, the principled approach, which is developed with consideration of these issues, is described. Through an illustrative problem, it is seen that when problem complexity is considered, the classification performance of the neural networks can be much better than what is observed. Furthermore, it is seen that model order selection processes based upon a single dataset can lead to an incorrect conclusion concerning the best model, which impacts model error and utility. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/S0167-9236(03)00093-9 | Decision Support Systems |
Keywords | Field | DocType |
neural network,artificial neural networks,principled approach,neural network classification model,data utilization,best model,model error,bayesian classification,model order selection,model bias,classification,model variance,illustrative problem,decision processes,model accuracy,classification performance,e-commerce,e commerce,decision support system,artificial neural network | Data mining,Neural network classification,Errors-in-variables models,Naive Bayes classifier,Computer science,Decision support system,Implementation,Artificial intelligence,Deep learning,Artificial neural network,Machine learning,E-commerce | Journal |
Volume | Issue | ISSN |
38 | 2 | Decision Support Systems |
Citations | PageRank | References |
8 | 0.63 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor L. Berardi | 1 | 39 | 2.32 |
| B. Eddy Patuwo | 2 | 261 | 19.98 |
| Michael Y. Hu | 3 | 426 | 55.74 |