Reducing Variance of Committee Prediction with Resampling Techniques

Bambang Parmanto, Paul W. Munro, Howard R. Doyle

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Algorithms for reducing variance in neural network prediction using committee and resampling techniques (bootstrap and cross-validation) are presented. Their effectiveness is tested on data sets with different levels of noise and on medical diagnosis data sets. The methods are most effective when the noise level in the data is high or the size of the learning set is small, which consequently produces high variance. The algorithms will not be of much help in cases where the error of prediction is mainly due to bias. An increase in bias is observed due to smaller effective learning size in the bootstrap and cross-validation committee. The impact of increased bias on the performance ranges from negligible to completely undermining the benefit of reducing the variance.

Original languageEnglish (US)
Pages (from-to)405-426
Number of pages22
JournalConnection Science
Issue number3-4
StatePublished - Dec 1996
Externally publishedYes


  • Bias-variance
  • Bootstrap
  • Committee
  • Cross-validation
  • Ensemble
  • Regularization
  • Resampling techniques

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Artificial Intelligence


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