Decision combination of multiple classifiers

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.