Name
Abstract | ||
|---|---|---|
Data splitting is an important consideration during artificial neural network (ANN) development where hold-out cross-validation is commonly employed to ensure generalization. Even for a moderate sample size, the sampling methodology used for data splitting can have a significant effect on the quality of the subsets used for training, testing and validating an ANN. Poor data splitting can result in inaccurate and highly variable model performance; however, the choice of sampling methodology is rarely given due consideration by ANN modellers. Increased confidence in the sampling is of paramount importance, since the hold-out sampling is generally performed only once during ANN development. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.neunet.2009.11.009 | Neural Networks |
Keywords | Field | DocType |
Artificial neural networks,Data splitting,Cross-validation,Self-organizing maps,Stratified sampling | Data set,Function approximation,Self-organizing map,Artificial intelligence,Sampling (statistics),Stratified sampling,Artificial neural network,Cross-validation,Mathematics,Sample size determination,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 2 | 0893-6080 |
Citations | PageRank | References |
27 | 1.33 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert J. May | 1 | 27 | 1.33 |
| Holger R. Maier | 2 | 738 | 72.97 |
| Graeme C. Dandy | 3 | 441 | 47.01 |