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Foto på Mattias Ohlsson

Mattias Ohlsson

Professor

Foto på Mattias Ohlsson

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

Författare

  • Michael Green
  • Mattias Ohlsson

Redaktör

  • 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.

Avdelning/ar

  • Beräkningsbiologi och biologisk fysik - Har omorganiserats

Publiceringsår

2007

Språk

Engelska

Publikation/Tidskrift/Serie

Third International Conference on Computational Intelligence in Medicine and Healthcare

Dokumenttyp

Konferensbidrag

Ämne

  • Biophysics

Nyckelord

  • 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

Aktiv

Published