Decision combination of multiple classifiers

Frank Y. Shih, Gang Fu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


In order to improve the performance in pattern classification, we utilize multiple classifiers and combine their individual decisions to make a final decision. In this paper, we present the combination using Bayesian method and compare minimum errors. This method requires the posteriori probabilities from all classifiers, which may be difficult to calculate in real world because tremendous amounts of training samples are needed. Alternatively, a confusion matrix is developed for approximation. We also use different combining rules for comparisons and apply them to handwritten digit recognition.

Original languageEnglish (US)
Pages (from-to)323-334
Number of pages12
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number2
StatePublished - Mar 2008

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • Classifier fusion
  • Handwritten digit recognition
  • Multiple classifiers
  • Pattern classification


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