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Mattias Ohlsson

Mattias Ohlsson


Mattias Ohlsson

Comparison of standard resampling methods for performance estimation of artificial neural network ensembles


  • Michael Green
  • Mattias Ohlsson


  • Emmanuel Ifeachor

Summary, in English

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.


  • Computational Biology and Biological Physics

Publishing year





Third International Conference on Computational Intelligence in Medicine and Healthcare

Document type

Conference paper


  • Biophysics


  • performance estimation
  • k-fold cross validation
  • bootstrap
  • artificial neural networks

Conference name

Third International Conference on Computational Intelligence in Medicine and Healthcare

Conference date

2007-07-25 - 2007-07-27