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Abstract
Estimation of the generalization performance for classification within the medical applications domain is always an important task. In this study we focus on artificial neural network ensembles as the machine learning technique. We present a numerical comparison between five common resampling techniques: k-fold cross validation (CV), holdout, using three cutoffs, and bootstrap using five different data sets. The results show that CV together with holdout $0.25$ and $0.50$ are the best resampling strategies for estimating the true performance of ANN ensembles. The bootstrap, using the .632+ rule, is too optimistic, while the holdout $0.75$ underestimates the true performance.
| Original language | English |
|---|---|
| Title of host publication | Third International Conference on Computational Intelligence in Medicine and Healthcare |
| Editors | Emmanuel Ifeachor |
| Number of pages | 6 |
| Publication status | Published - 2007 |
| Event | Third International Conference on Computational Intelligence in Medicine and Healthcare - Plymouth, England Duration: 2007 Jul 25 → 2007 Jul 27 |
Conference
| Conference | Third International Conference on Computational Intelligence in Medicine and Healthcare |
|---|---|
| Period | 2007/07/25 → 2007/07/27 |
Subject classification (UKÄ)
- Biophysics
Free keywords
- performance estimation
- k-fold cross validation
- bootstrap
- artificial neural networks
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Dive into the research topics of 'Comparison of standard resampling methods for performance estimation of artificial neural network ensembles'. Together they form a unique fingerprint.Research output
- 1 Doctoral Thesis (compilation)
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Improving diagnosis of acute coronary syndromes in an emergency setting: A machine learning approach
Green, M., 2008, 147 p.Research output: Thesis › Doctoral Thesis (compilation)
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