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Fixed a bug under misclassification error case in cvmultnet.m, pointed out by Sungeun Kim from Indiana University.
Computing risk-based misclassification error density distribution for ensembles is an important yet difficult task.
Real-world datasets and holdout samples are used to illustrate computation of posterior misclassification error distributions.
The experimental results demonstrate that the misclassification error is very small between the proposed result and hand drawing.
The proposed method uses an absolute and squared approximation of the misclassification error rate to design a linear classifier.
Because the Bayesian classifier is optimal in the sense of total misclassification error, it should outperform the neural network.
Recently, novel designs allow the reduction of exposure misclassification error for a more accurate assessment of individual exposure.
The performance of a proposed model is carried out in terms of coverage, misclassification error and accuracy.
Moreover, our results showed that no improvement was obtained in prediction accuracy of DTA algorithm with minimizing taxonomic distance compared to minimizing misclassification error (0.71).
Fig. 3 Misclassification error rate.
Table 2 shows the corresponding misclassification error rate.
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